Applied mathematics Books
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Mathematics and Culture III
Book SynopsisThis work contains the proceedings of the "Mathematics and Culture" conference held in Venice in March 2002. The conference aims to act as a bridge across the various aspects of human knowledge. While keeping mathematics as its core, it is aimed at anyone endowed with cultural curiosity and interests, whether within or (even more so) outside mathematics. This volume therefore covers music, cinema, art, theatre and literature, with topics ranging from Tibet to comics.Trade ReviewFrom the reviews:“This is a collection of papers that highlight the relation between mathematics and culture in the broadest sense. … it is an eye-opener to many who might experience mathematics as an invention to terrorise children at school. … It is an excellent tool to raise public awareness of mathematics. It can be easily used by teachers or lecturers as a Trojan horse to conquer the fortress of the less mathematically inclined.” (A. Bultheel, The European Mathematical Society, October, 2012)Table of ContentsI Mathematicians: Open Your Eyes Through Mathematics by Emma Castelnuovo.- The Theory of Motion from Hellenism to the 20th Century by Giovanni Gallavotti.- How Mathematics Helps Us Avoid Biases by Aljoša Volcic.- II Mathematics and Music: Mathematical Modelling of Musical Sounds by Giovanni De Poli.- What Time-Frequency Analysis Can Do to Music Signals (and What It Can’t Do) by Monica Dörfler.- … Listen:… By Laura Tedeschini Lalli.- Being an Artist with Mathematics and a Computer by Stefano Busello.- Escher-Like Perspectives and Music Production by Claudio Ambrosini- III Mathematics and Art: Complexity in Art: Klee, Duchamp and Escher by Roberto Giunti.- Stayin’ Alive (Just Barely): The Fate of the Geometrical Fourth Dimension at Mid-Century by Linda Dalrypmple Henderson.- The pleasure of threads: The Visual Experience of Fred Sandback’s sculptures by Manuel Corrada.- Paladino’s Mathematicians by Enzo di Martino.- IV Mathematics and Cinema: Mathematics in the Movies: A Case Study by Harold W. Kuhn.- V Mathematics and Venice: Luca Pacioli and Venice by Giovanni Fazzini.- A Venetian Comic Book by Luca Boschi, Michele Emmer.- Labyrinths by Michele Emmer, Gian Marco Todesco.- The Romance of Double-Entry Bookkeeping by Anthony Phillips.- VI Peking 2002: Is Chinese Mathematics Chinese? by Jean-Claude Martzloff.- Why Mathematics in Ancient China? by Anjing Qu.- The "Lack of Grounding" of Chinese Astronomy: a Communis Opinio of XVII century Europe by Francesco D’Arelli.- A Mathematician in Lhasa by Michele Emmer.- VII Mathematics and Theatre: Infinity and the Search for Simplicity by Sergio Escobar.- VIII Mathematics and Comic Strips: Digital Character Construction in Walt Disney Pictures' Feature "Dinosaur" by Stewart Dickson.- Comics and MathMagic: Notes on Disney Numerology by Luca Boschi
£42.74
Springer Fachmedien Wiesbaden 3D-Bildsegmentierung mittels statistischer Formmodelle: Korrespondenzfindung, Modellierung, Segmentierung und ihre wechselseitigen Abhängigkeiten
Book SynopsisSebastian T. Gollmer entwickelt neue Methoden und Algorithmen für die Erstellung statistischer Formmodelle, die Formmodellierung und die formmodellbasierte Bildsegmentierung. Der Autor diskutiert ihre Vorteile gegenüber den jeweils etablierten Verfahren aus der Literatur und evaluiert den generellen Einfluss dieser drei Aspekte auf die erzielbare Segmentierungsgenauigkeit. Letzteres erfolgt sowohl unter Verwendung neu entwickelter und etablierter Evaluierungsverfahren als auch im Rahmen realer Anwendungen. Von besonderer praktischer Relevanz zeigen sich dabei die exzellenten, mit einem neuen vollautomatischen Algorithmus erzielten Ergebnisse für die Unterkiefersegmentierung.Table of ContentsStatistische Formmodelle.- Evaluierung der Korrespondenzgüte.- Untersuchung der Normalverteilungsannahme.- Kernbasierte Formmodellierung.- Relaxiertes aktives Formmodell.- Unterkiefer- und Abdomensegmentierung.
£47.49
Springer Fachmedien Wiesbaden Mindestanforderungen an die Mathematik-Kenntnisse
Book SynopsisDem Leser werden neben praxisnahen Beispielen zu jedem Thema auch zahlreiche Übungsaufgaben mit Lösungen zur Verfügung gestellt. Somit kann der zukünftige Studierende sich zunächst orientieren, ob seine Fähigkeiten für das gewünschte Ingenieurstudium bereits ausreichend sind oder ob er mehr hierfür tun muss.Table of ContentsMathematische Grundlagen.- Elementare Geometrie.- Funktionen.- Differentialrechnung.- Integralrechnung.- Vektorrechnung.- Matrizenrechnung.- Wahrscheinlichkeits- und Fehlerrechnung.- Folgen und Reihen.- Ausblick: Komplexe Zahlen und Differentialgleichungen.
£32.99
Springer Fachmedien Wiesbaden Übungsbuch Mathematik für Fachschule Technik und
Book SynopsisDieses Übungsbuch bringt viele Beispielaufgaben aus der Technik mit sehr ausführlichem Lösungsweg, vermittelt den Stoff anwendungsorientiert und ermöglicht ein erfolgreiches Selbststudium. Es führt zur Hochschulreife und eignet sich hervorragend zur Vorbereitung auf das technische Studium an Hochschulen. Viele Aufgaben unterschiedlichen Schwierigkeitsgrades sichern den Lernerfolg. Die Abschnitte zu Rotationsvolumen, Vektorprodukte, Lage von Geraden, Ebenengleichungen sowie das Potenzieren und Radizieren von komplexen Zahlen wurden erweitert.Table of ContentsElementare Rechenoperationen.- Algebraische Gleichungen.- Ungleichungen.- Gleichungssysteme.- Lineares Optimieren.- Exponential- und Logarithmusgleichungen.- Längenberechnung am Dreieck.- Trigonometrie.- Analytische Geometrie.- Flächen- und Volumenberechnungen.- Funktionen und Relationen.- Differentiation von Funktionen.- Anwendung der Differentialrechnung.- Exponentialfunktionen.- Flächen- und Volumenberechnung mittels Integralrechnung.- Vektoralgebra.- Vektorrechnung.- Komplexe Rechnung.
£28.88
Springer Fachmedien Wiesbaden Signale und Systeme: Theorie, Simulation,
Book SynopsisDas Buch behandelt die Theorie der Signale und (linearen) Systeme sowie ihrer Anwendungen. Nach einer Einführung anhand von Beispielen aus den verschiedenen Anwendungsgebieten werden die Grundtechniken zur Beschreibung zeitkontinuierlicher linearer zeitinvarianter Systeme und deren Wirkung auf Signale diskutiert. Der Übergang in die digitale Signalverarbeitung wird durch die Herleitung und Diskussion des Abtasttheorems vorbereitet. Anschließend werden die Methoden der Systemtheorie für die digitale Signalverarbeitung vorgestellt. Ein Schwerpunkt liegt dabei auf der Diskussion der Diskreten Fouriertransformation. Hier stehen insbesondere die Zusammenhänge zwischen DFT/FFT-Spektren und den Spektren der zeitkontinuierlichen Signale im Focus. Die behandelten Methoden werden auf die Verarbeitung stochastischer Signale übertragen und damit für die praktische Anwendung nutzbar gemacht. Der Autor beschreibt zahlreiche reale Beispiele mit echten gemessenen Daten und stellt das Material sowie die zugehörigen MATLAB-Programme online zu Verfügung. Das Buch enthält über 150, in vielen Fällen MATLAB/Simulink-basierte Übungsaufgaben, deren Lösungen in einem eigenen Lösungsband zur Verfügung stehen. Für die 3. Auflage wurden sowohl im Lehrbuch als auch im Lösungsbuch verwendete Bezeichnungen harmonisiert und vereinheitlicht. Alle verwendeten MATLAB-Funktionen und Simulink-Systeme wurden nochmals überarbeitet und an die aktuelle MATLAB-Version angepasst. Sämtliche Grafiken wurden neu überarbeitet. Größen, Schriftart und Schriftgröße wurden vereinheitlicht, um eine bessere Lesbarkeit der Grafiken zu erzielen. Darüber hinaus wurden einige wenige, immer noch vorhandene Fehler aus den Texten eliminiert.Das Buch eignet sich prinzipiell für Studierende aller ingenieurwissenschaftlichen Fachrichtungen und spricht explizit auch die maschinenbaunahen Bereiche an. Aufgrund der ausführlichen Darstellung der Grundlagen ist es jedoch auch für Elektro- und Nachrichtentechniker gewinnbringend nutzbar.Table of ContentsEinführungsbeispiele und grundlegende Begriffe.- Analoge Signale und Systeme.- Abtastung und Digitalisierung.- Digitale Signale und Systeme.- LTI-Systeme und Stochastische Signale.- Mathematische Grundlagen und Tabellen.- Literaturverzeichnis.- Begleitsoftwareindex MATLAB-Softwareindex.
£37.99
River Publishers Introduction to Quantum Computing
Book SynopsisThis book explores quantum computing as a transformative technology and its applications in cryptography, teleportation, IoT, AI, Blockchain, and the futurist concept of quantum internet. It explains the fundamentals of quantum computing and how it’s different from classic computing. The challenges facing quantum computing will be discussed, and the types of quantum computing will be introduced and explained. The concept and types of Qubit and its implications on quantum computing applications will be explained.Table of Contents1. What is Quantum Computing? 2. Quantum Cryptography 3. Quantum Internet 4. Quantum teleportation 5. Quantum Computing and IoT 6. Quantum Computing and Blockchain: Myths and Facts 7. Quantum Computing and AI: A Mega-Buzzword 8. Quantum Computing Trends
£29.99
A A Balkema Publishers Unsteady Flow and Fluid Transients
Book SynopsisA collection of papers on the prediction of unsteady flow and fluid transients in a wide variety of systems. Coverage ranges from theory to practical application of techniques, and the work describes failures as well as successes.
£247.00
Taylor & Francis Ltd Mathematical Problems and Methods of Hydrodynamic
Book SynopsisThe material provides an historical background to forecasting developments as well as introducing recent advances. The book will be of interest to both mathematicians and physicians, the topics covered include equations of dynamical meteorology, first integrals, non-linear stability, well-posedness of boundary problems, non-smooth solutions, parameters and free oscillations, meteorological data processing, methods of approximation and interpolation and numerical methods for forecast modelling.Table of Contents1. Equations of Dynamical Meteorology 2. Small Parameters and Small Oscillations 3. Meteorological Data Processing 4. Numerical Methods for Prognostic Systems
£446.50
The University of Chicago Press Perspectives in Computation
Book SynopsisComputation is the process of applying a procedure or algorithm to the solution of a mathematical problem. This book covers three broad topics: the computation process and its limitations, the search for computational efficiency, and the role of quantum mechanics in computation.Trade Review"A beautiful little book.... It succeeds so well because Geroch believes that 'physics is a human activity' and wants to share some of its joy with others." - Physics Today"
£28.00
The University of Chicago Press Deviant Logic Fuzzy Logic Beyond the Formalism
Book SynopsisIn this volume, Haack includes the complete text of Deviant Logic, as well as five additional papers that expand and update it. Two of these essays critique fuzzy logic, while three augment Deviant Logic's treatment of deduction and logical truth.
£30.00
The University of Chicago Press Several Complex Variables
Book SynopsisDrawn from lectures given by Raghavan Narasimhan at the University of Geneva and the University of Chicago, this book presents the part of the theory of several complex variables pertaining to unramified domains over C . Topics discussed are Hartogs' theory, domains in holomorphy, and automorphism of bounded domains.
£38.00
Columbia University Press The Why of Things
Book SynopsisFrom the author of the landmark bestseller The-Thirty-Six-Hour-Day comes a lucid, engaging, and nuanced treatment of one of the essential questions in science, medicine, and life: "Why?"Trade ReviewPeter Rabins shows incredible breadth of knowledge and his thesis-that there are three distinct approaches to causation, appropriate for different types of questions-is compelling. His writing is engaging, and the subject matter is deeply relevant. -- Simon Levin, Princeton University, author of Fragile Dominion: Complexity and the Commons Peter Rabin's book draws upon science, statistics, philosophy, and religion to stretch readers' thinking about the 'why' and 'how' of what happens. It provides a remarkably lucid synthesis of diverse ideas about causality based on superb scholarship and is always entertaining. I heartily recommend it. -- David Reuben, MD, David Geffen School of Medicine, University of California, Los Angeles From the two year old child's endlessly nested 'why' questions to the Old Testament and the modern scientist, and through many philosophers in between, Peter Rabins takes us on a fascinating quest in search of answers to that seemingly simplest of all questions: Why? Simple but enigmatic because, like the two year old, how do we know when to be satisfied and how do we know when we know? Throughout The Why of Things, Rabins examines fundamental aspects of how we know-or don't. In his erudite yet accessible book, readers will learn everything from philosophical categorization to nonlinear dynamics in a way that will suddenly make sense, even if they never do find out exactly why. -- Stuart Firestein, Columbia University, author of Ignorance: How It Drives Science if you're looking to learn how to better reason things out through logic and comparative analysis, then this one may be for you. Lifelong Dewey Blog Quite simply, wow. This is one of the most complex, mind-boggling and ultimately satisfying books I have read in a very long time. The Garden Window Blog A most enjoyable read and source of inspiration. The book constitutes a noteworthy addition to Professor Rabins' academic production... Philosophers of science - and perhaps more specifically philosophers interested in causality, explanation, or medicine - would gain a lot in reading it. MetascienceTable of ContentsPreface Introduction 1. Historical Overview: The Four Approaches to Causality 2. The Three-Facet Model: An Overview 3. The Answer Is Either "No" or "Yes": Causality as a Categorical Concept 4. Probabilities 5. A Third Model of Causality: The Emergent 6. Empirical: The Physical Sciences 7. Empirical: The Biological Sciences 8. Empirical: Epidemiology 9. Narrative Truth: The Empathic Method 10. Cause in the Ecclesiastic Tradition 11. Seeking the Why of Things: The Model Applied References Index
£58.77
Columbia University Press The Why of Things
Book SynopsisFrom the author of the landmark bestseller The-Thirty-Six-Hour-Day comes a lucid, engaging, and nuanced treatment of one of the essential questions in science, medicine, and life: "Why?"Trade ReviewPeter Rabins shows incredible breadth of knowledge and his thesis-that there are three distinct approaches to causation, appropriate for different types of questions-is compelling. His writing is engaging, and the subject matter is deeply relevant. -- Simon Levin, Princeton University, author of Fragile Dominion: Complexity and the Commons Peter Rabin's book draws upon science, statistics, philosophy, and religion to stretch readers' thinking about the 'why' and 'how' of what happens. It provides a remarkably lucid synthesis of diverse ideas about causality based on superb scholarship and is always entertaining. I heartily recommend it. -- David Reuben, MD, David Geffen School of Medicine, University of California, Los Angeles From the two year old child's endlessly nested 'why' questions to the Old Testament and the modern scientist, and through many philosophers in between, Peter Rabins takes us on a fascinating quest in search of answers to that seemingly simplest of all questions: Why? Simple but enigmatic because, like the two year old, how do we know when to be satisfied and how do we know when we know? Throughout The Why of Things, Rabins examines fundamental aspects of how we know-or don't. In his erudite yet accessible book, readers will learn everything from philosophical categorization to nonlinear dynamics in a way that will suddenly make sense, even if they never do find out exactly why. -- Stuart Firestein, Columbia University, author of Ignorance: How It Drives Science if you're looking to learn how to better reason things out through logic and comparative analysis, then this one may be for you. Lifelong Dewey Blog Quite simply, wow. This is one of the most complex, mind-boggling and ultimately satisfying books I have read in a very long time. The Garden Window Blog A most enjoyable read and source of inspiration. The book constitutes a noteworthy addition to Professor Rabins' academic production... Philosophers of science - and perhaps more specifically philosophers interested in causality, explanation, or medicine - would gain a lot in reading it. MetascienceTable of ContentsPreface Introduction 1. Historical Overview: The Four Approaches to Causality 2. The Three-Facet Model: An Overview 3. The Answer Is Either "No" or "Yes": Causality as a Categorical Concept 4. Probabilities 5. A Third Model of Causality: The Emergent 6. Empirical: The Physical Sciences 7. Empirical: The Biological Sciences 8. Empirical: Epidemiology 9. Narrative Truth: The Empathic Method 10. Cause in the Ecclesiastic Tradition 11. Seeking the Why of Things: The Model Applied References Index
£16.14
Columbia University Press A Survival Guide to the Misinformation Age
Book SynopsisA step-by-step strategy for protecting ourselves against the phony claims, trendy pseudoscience, and sloppy thinking that permeate our world.Trade ReviewA Survival Guide to the Misinformation Age is a no-holds-barred paean to the scientific mode of thinking. Helfand's wide-ranging, interdisciplinary, humorously cynical intellect comes through at every turn. -- J. Craig Wheeler, University of Texas at Austin A Survival Guide for the Misinformation Age is an impassioned plea for science literacy. Given the state of the world today, in which scientifically underinformed voters elect scientifically illiterate politicians, David Helfand has written the right book at the right time with the right message. Read it now. The future of our civilization may depend on it. -- Neil deGrasse Tyson, astrophysicist, American Museum of Natural History David Helfand's Survival Guide to the Misinformation Age gives readers a chance to spend time with one this country's clearest and best critical thinkers. Helfand channels Steven Pinker's ability to dissect language with John Alan Paulos's ability to explain numbers with Richard Dawkins' ability to explain our existence (to obtain food, to avoid being food, and to reproduce) with George Carlin's ability to make us laugh. Using personal anecdotes (he's a Red Sox fan), Helfand teaches us how to think through questions as diverse as why the moon doesn't make us lunatics to why it only takes twenty-three people to have a 50:50 chance that two will have the same birthday. A real pleasure. -- Paul Offit, University of Pennsylvania Important and timely. Library Journal Helfand's work is an admirable response to a long-standing problem of sloppy thinking. Publishers Weekly Helfand is a man brimming with incredible insights on the universe. Dave's Universe A must-read for anyone presuming to call themselves a scientist and a should-read for anyone just trying to make sense of the overwhelming volume of data and real and concocted 'proofs' of nearly everything that spews forth from the Internet on demand. This book provides a road map for teaching students how to both celebrate science and how to view their primary source of information with skepticism and caution. Every science teacher should read this book. -- John Ziegler NSTA Recommends For those with an arts and humanities background, this book offers many valuable lessons... For everyone else it provides a vital antidote to the ills of misinformation by teaching systematic and rigorous scientific reasoning. -- Marina Gerner Times Literary Supplement Highly recommended. CHOICE How I wish everyone would read, appreciate, and follow [David J. Helfand's] guidance. Physics TodayTable of ContentsForeword Acknowledgments Introduction: Information, Misinformation, and Our Planet's Future 1. A Walk in the Park 2. What Is Science? 3. A Sense of Scale Interlude 1: Numbers 4. Discoveries on the Back of an Envelope 5. Insights in Lines and Dots Interlude 2: Language and Logic 6. Expecting the Improbable 7. Lies, Damned Lies, and Statistics 8. Correlation, Causation ... Confusion and Clarity 9. Definitional Features of Science 10. Applying Scientific Habits of Mind to Earth's Future 11. What Isn't Science 12. The Triumph of Misinformation; The Peril of Ignorance 13. The Unfinished Cathedral Appendix: Practicing Scientific Habits of Mind Notes Index
£58.77
Columbia University Press A Survival Guide to the Misinformation Age
Book SynopsisA step-by-step strategy for protecting ourselves against the phony claims, trendy pseudoscience, and sloppy thinking that permeate our world.Trade ReviewA Survival Guide to the Misinformation Age is a no-holds-barred paean to the scientific mode of thinking. Helfand's wide-ranging, interdisciplinary, humorously cynical intellect comes through at every turn. -- J. Craig Wheeler, University of Texas at Austin A Survival Guide for the Misinformation Age is an impassioned plea for science literacy. Given the state of the world today, in which scientifically underinformed voters elect scientifically illiterate politicians, David Helfand has written the right book at the right time with the right message. Read it now. The future of our civilization may depend on it. -- Neil deGrasse Tyson, astrophysicist, American Museum of Natural History David Helfand's Survival Guide to the Misinformation Age gives readers a chance to spend time with one this country's clearest and best critical thinkers. Helfand channels Steven Pinker's ability to dissect language with John Alan Paulos's ability to explain numbers with Richard Dawkins' ability to explain our existence (to obtain food, to avoid being food, and to reproduce) with George Carlin's ability to make us laugh. Using personal anecdotes (he's a Red Sox fan), Helfand teaches us how to think through questions as diverse as why the moon doesn't make us lunatics to why it only takes twenty-three people to have a 50:50 chance that two will have the same birthday. A real pleasure. -- Paul Offit, University of Pennsylvania Important and timely. Library Journal Helfand's work is an admirable response to a long-standing problem of sloppy thinking. Publishers Weekly Helfand is a man brimming with incredible insights on the universe. Dave's Universe A must-read for anyone presuming to call themselves a scientist and a should-read for anyone just trying to make sense of the overwhelming volume of data and real and concocted 'proofs' of nearly everything that spews forth from the Internet on demand. This book provides a road map for teaching students how to both celebrate science and how to view their primary source of information with skepticism and caution. Every science teacher should read this book. -- John Ziegler NSTA Recommends For those with an arts and humanities background, this book offers many valuable lessons... For everyone else it provides a vital antidote to the ills of misinformation by teaching systematic and rigorous scientific reasoning. -- Marina Gerner Times Literary Supplement Highly recommended. CHOICE How I wish everyone would read, appreciate, and follow [David J. Helfand's] guidance. Physics TodayTable of ContentsForeword Acknowledgments Introduction: Information, Misinformation, and Our Planet's Future 1. A Walk in the Park 2. What Is Science? 3. A Sense of Scale Interlude 1: Numbers 4. Discoveries on the Back of an Envelope 5. Insights in Lines and Dots Interlude 2: Language and Logic 6. Expecting the Improbable 7. Lies, Damned Lies, and Statistics 8. Correlation, Causation ... Confusion and Clarity 9. Definitional Features of Science 10. Applying Scientific Habits of Mind to Earth's Future 11. What Isn't Science 12. The Triumph of Misinformation; The Peril of Ignorance 13. The Unfinished Cathedral Appendix: Practicing Scientific Habits of Mind Notes Index
£18.04
John Wiley & Sons Inc Handbook of Applied Algorithms
Book SynopsisDiscover the benefits of applying algorithms to solve scientific, engineering, and practical problems Providing a combination of theory, algorithms, and simulations, Handbook of Applied Algorithms presents an all-encompassing treatment of applying algorithms and discrete mathematics to practical problems in hot application areas, such as computational biology, computational chemistry, wireless networks, and computer vision. In eighteen self-contained chapters, this timely book explores: * Localized algorithms that can be used in topology control for wireless ad-hoc or sensor networks * Bioinformatics algorithms for analyzing data * Clustering algorithms and identification of association rules in data mining * Applications of combinatorial algorithms and graph theory in chemistry and molecular biology * Optimizing the frequency planning of a GSM network using evolutioTable of ContentsPreface. Abstracts. Contributors. 1. Generating All and Random Instances of A combinatorial Object (Ivan Stojmenovic) 2. Backtracking and Isomorph-Free Generation of Polyhexes (Lucia Moura and Ivan Stojmenovic) 3. Graph Theoretic Models in Chemistry and Molecular Biology (Debra Knisley and Jeff Knisley) 4. Algorithmic Methods for the Analysis of Gene Expression Data (Hongbo Xie, Uros Midic, Slobodan Vucetic, and Zoran Obradovic) 5. Algorithms of Reaction-Diffusion Computing (Andrew Adamatzky) 6. Data Mining Algorithms I: Clustering (Dan A. Simovici) 7. Data Mining Algorithms II: Frequent Item Sets (Dan A. Simovici) 8. Algorithms for Data Streams (Camil Demetrescu and Irene Finocchi) 9. Applying Evolutionary Algorithms to Solve the Automatic Frequency Planning Problem (Francisco Luna, Enrique Alba, Antonio J. Nero, Patrick Nauru, and Salvador Pedraza) 10. Algorithmic Game Theory and Application s(Marios Mavronicolas, Vicky Papdopoulou, and Paul Spirakis) 11. Algorithms for Real-Time Object Detection in Images (Milos Stojmenovic) 12. 2D Shape Measures for Computer Vision (Paul L. Rosin and Jovisa Zunic) 13. Cryptographic Algorithms (Binal Roy and Amiya Nayak) 14. Secure Communication in Distributed Sensor Networks (DSN) (Subhamoy Maitra and Bimal Roy) 15. Localized Topology Control Algorithms for Ad Hoc and Sensor Networks (Hannes Frey and David Simplot-Ryl) 16. A Novel Admission Control for Multimedia LEO Satellite Networks (Syed R. Rizvi, Stephan Olariu, and Mona E. Rizvi) 17. Resilient Recursive Routing in Communication Networks (Costas C. Constantinou, Alexander S. Stepanenko, Theodoros N. Arvanitis, Kevin J. Baughan, and Bin Liu) 18. Routing Algorithms on WDM Optical Networks (Qian-Ping Gu) Index.
£110.66
John Wiley & Sons Inc Machine Learning in Bioinformatics
Book SynopsisMachine learning techniques such as Markov models, support vector machines, neural networks, graphical models, etc. , have been successful in analyzing life science data because of their capabilities of handling randomness and uncertainties of data and noise and in generalization.Table of ContentsForeword. Preface. Contributors. 1 Feature Selection for Genomic and Proteomic Data Mining (Sun-Yuan Kung and Man-Wai Mak). 2 Comparing and Visualizing Gene Selection and Classification Methods for Microarray Data (Rajiv S. Menjoge and Roy E. Welsch). 3 Adaptive Kernel Classifiers Via Matrix Decomposition Updating for Biological Data Analysis (Hyunsoo Kim and Haesun Park). 4 Bootstrapping Consistency Method for Optimal Gene Selection from Microarray Gene Expression Data for Classification Problems (Shaoning Pang, Ilkka Havukkala, Yingjie Hu, and Nikola Kasabov). 5 Fuzzy Gene Mining: A Fuzzy-Based Framework for Cancer Microarray Data Analysis (Zhenyu Wang and Vasile Palade). 6 Feature Selection for Ensemble Learning and Its Application (Guo-Zheng Li and Jack Y. Yang). 7 Sequence-Based Prediction of Residue-Level Properties in Proteins (Shandar Ahmad, Yemlembam Hemjit Singh, Marcos J. Araúzo-Bravo, and Akinori Sarai). 8 Consensus Approaches to Protein Structure Prediction (Dongbo Bu, ShuaiCheng Li, Xin Gao, Libo Yu, Jinbo Xu, and Ming Li). 9 Kernel Methods in Protein Structure Prediction (Jayavardhana Gubbi, Alistair Shilton, and Marimuthu Palaniswami). 10 Evolutionary Granular Kernel Trees for Protein Subcellular Location Prediction (Bo Jin and Yan-Qing Zhang). 11 Probabilistic Models for Long-Range Features in Biosequences (Li Liao). 12 Neighborhood Profile Search for Motif Refinement (Chandan K. Reddy, Yao-Chung Weng, and Hsiao-Dong Chiang). 13 Markov/Neural Model for Eukaryotic Promoter Recognition (Jagath C. Rajapakse and Sy Loi Ho). 14 Eukaryotic Promoter Detection Based on Word and Sequence Feature Selection and Combination (Xudong Xie, Shuanhu Wu, and Hong Yan). 15 Feature Characterization and Testing of Bidirectional Promoters in the Human Genome—Significance and Applications in Human Genome Research (Mary Q. Yang, David C. King, and Laura L. Elnitski). 16 Supervised Learning Methods for MicroRNA Studies (Byoung-Tak Zhang and Jin-Wu Nam). 17 Machine Learning for Computational Haplotype Analysis (Phil H. Lee and Hagit Shatkay). 18 Machine Learning Applications in SNP–Disease Association Study (Pritam Chanda, Aidong Zhang, and Murali Ramanathan). 19 Nanopore Cheminformatics-Based Studies of Individual Molecular Interactions (Stephen Winters-Hilt). 20 An Information Fusion Framework for Biomedical Informatics (Srivatsava R. Ganta, Anand Narasimhamurthy, Jyotsna Kasturi, and Raj Acharya). Index.
£110.66
John Wiley & Sons Inc Mathematics for the Green Industry
Book SynopsisGet this comprehensive guide to the use of math in the Green Industry. Designed for both students and practitioners in the Green Industry, this book offers full coverage of the calculations necessary to effectively, safely, and economically manage a Green Industry operation. The authors provide clear explanations of all relevant mathematical principles and cover calculations inherent in all aspects of the Green Industry, from determining area and volume, to the application of fertilizers, pesticides, and growth regulators, to preparing design and installation cost estimates. Coverage includes computations for: Landscape installation and maintenance. Greenhouse, nursery, and interior landscape operation. Parks and recreation maintenance. Turf management, including lawn care, sports turf, and sod production. Proper application of fertilizers, pesticides, and plant-growth regulators. Proper calibration of application equipment.Table of ContentsPreface v About the Authors vii Part 1 Mathematical Principles 1 Chapter 1 Basic Math Skills 1 Chapter 2 Measurement and Calculations with Measured Values 31 Chapter 3 Geometry 47 Part 2 Green Industry Applications 79 Chapter 4 Calculating the Area of Landscape Features 79 Chapter 5 Fertilizer Calculations 95 Chapter 6 Pesticide and Plant Growth Regulator Calculations 127 Chapter 7 Calibration of Application Equipment 151 Chapter 8 Mathematical Applications for the Turfgrass Industry 169 Chapter 9 Mathematical Applications for the Landscape Industry 191 Chapter 10 Mathematical Applications for the Greenhouse, Nursery, and Interior Landscape Industries 223 Appendix A: Metric System Prefixes 285 Appendix B: Tables of Equivalents 287 Appendix C: Table of Conversion Factors 297 Appendix D: Squaring-Up Gardens and Garden Structures 307 Appendix E: Solutions to Practice Problems 309 Index 395
£59.36
John Wiley & Sons Inc Mathematical Bioeconomics
Book SynopsisOverall, this is an appealing work for students and professionals, and is certain to remain as one of the key works in natural resource analysis. Mathematical Reviews Biological renewable resources, essential to the survival of mankind, are increasingly overexploited by individuals and corporations that often sacrifice long-term economic health and sustainability for short-term gains. Mathematical Bioeconomics: The Mathematics of Conservation, Third Edition analyzes the economic forces underlying these misuses of renewable resources and discusses more effective methods of resource management. Promoting a complete understanding of general principles, the book allows readers to discover how rigorous mathematical models that incorporate both economic and biological factors should replace intuitive arguments for conservation and sustainability. This Third Edition continues to combine methodologies from the fields of economics, biology, and matheTable of ContentsPreface. Acknowledgments. 1 A Generic Bioeconomic Model. 1.1 What is Conservation? 1.2 What is a Model? 1.3 A Dynamic Resource-Harvesting Model. 1.4 A Bioeconomic Model. 1.5 A Dynamic Optimization Model. 1.6 A Model of Individual Behavior. 1.7 Individual Vessel Quotas. 1.8 The Veil of Uncertainty. 1.9 Other Resources. 2 Dynamic Optimization. 2.1 Constrained Optimization. 2.2 Optimal Control Theory in One Dimension. 2.3 Nonlinear Control Problems. 2.4 Discrete-time Optimal Control. 2.5 Appendix. 3 Basic Economic Concepts. 3.1 Interest and Discounting. 3.2 Supply and Demand. 3.3 Demand-limited Bionomic Equilibrium. 3.4 Optimal Harvesting Strategies. 3.5 External Costs. 3.6 Competition, Cooperation, and the Theory of Games. 3.7 The Economics of Uncertainty. 4 Investing in Harvesting Capacity. 4.1 Optimal Harvesting Capacity. 4.2 Investment Decisions under Competition. 4.3 Eliminating Excess Capacity. 4.4 Appendix: Optimal Investment. 5 Regulation of Renewable Resource Harvesting. 5.1 The Consequences of Unregulated Resource Harvesting. 5.2 Methods of Regulating Resource Harvesting. 5.3 Shadow Prices, Taxes and Tradeable Quotas. 5.4 Regulation without Taxes or Tradeable Quotas. 6 Growth and Aging. 6.1 Forestry Models. 6.2 Fisheries: The Cohort Model. 6.3 Multicohort Fisheries. 7 Resource Management under Uncertainty. 7.1 Process and Observational Uncertainty. 7.2 Understanding Uncertainty. 7.3 Process Uncertainty. 7.4 An Introduction to Decision Analysis. 7.5 Economic Uncertainties. 7.6 Appendix. 8 Disaggregated Resource Models. 8.1 Source-sink Models. 8.2 Predator-prey Models. 8.3 Mixed-species Harvesting. 9 Synopsis. Problem Solutions. References. Index.
£95.36
John Wiley & Sons Inc Statistical Distributions
Book SynopsisA new edition of the trusted guide on commonly used statistical distributions Fully updated to reflect the latest developments on the topic, Statistical Distributions, Fourth Edition continues to serve as an authoritative guide on the application of statistical methods to research across various disciplines. The book provides a concise presentation of popular statistical distributions along with the necessary knowledge for their successful use in data modeling and analysis. Following a basic introduction, forty popular distributions are outlined in individual chapters that are complete with related facts and formulas. Reflecting the latest changes and trends in statistical distribution theory, the Fourth Edition features: A new chapter on queuing formulas that discusses standard formulas that often arise from simple queuing systems Methods for extending independent modeling schemes to the dependent case, covering techniques for geneTrade Review"Overall, an excellent book for readers interested in qualitative data analysis. Highly recommended. Upper-division undergraduates through professionals." (Choice, 1 October 2011) "This new edition continues to illustrate the application of statistical methods to research across various disciplines, including medicine, engineering, business/finance, and the social sciences. Thoroughly revised and updated, the authors have refreshed this book to reflect the changes and current trends in statistical distribution theory that have occured since the publication of the previous edition eight years ago . . . key facts and formulas for forty major probability distributions are presented, making the book an ideal introduction to the general theory of statistical distributions as well as a quick reference on its basic principles". (MyCFO, 22 December 2010) "This new edition continues to illustrate the application of statistical methods to research across various disciplines, including medicine, engineering, business/finance, and the social sciences. Thoroughly revised and updated, the authors have refreshed this book to reflect the changes and current trends in statistical distribution theory that have occured since the publication of the previous edition eight years ago. The introductory chapters introduce the fundamental concepts of the distributions and the relationships between variables. For each distribution that follows, the key formulae, tables and diagrams are presented in a concise, user-friendly format. Key facts and formulas for forty major probability distributions are presented, making the book an ideal introduction to the general theory of statistical distributions as well as a quick reference on its basic principles". (MyCFO, 22 December 2010) Table of Contents1 Introduction. 2 Terms and Symbols. 2.1 Probability, Random Variable, Variate and Number. 2.2 Range, Quantile, Probability and Domain. 2.3 Distribution Function and Survival Function. 2.4 Inverse Distribution and Inverse Survival Function. 2.5 Probability Density Function and Probability Function. 2.6 Other Associated Functions and Quantities. 3 General Variate Relationships. 3.1 Introduction. 3.2 Function of a Variate. 3.3 One-to-One Transformations and Inverses. 3.4 Variate Relationships Under One-to-One Transformation. 3.5 Parameters, Variate, and Function Notation. 3.6 Transformation of Location and Scale. 3.7 Transformation from the Rectangular Variate. 3.8 Many-to-One Transformations. 4 Multivariate Distributions. 4.1 Joint Distributions. 4.2 Marginal Distributions. 4.3 Independence. 4.4 Conditional Distributions. 4.5 Bayes' Theorem. 4.6 Functions of a Multivariate. 5 Stochastic Modeling. 5.1 Introduction. 5.2 Independent Variates. 5.3 Mixture Distributions. 5.4 Skew-Symmetric Distributions. 5.5 Conditional Skewness. 5.6 Dependent Variates. 6 Parameter Inference. 6.1 Introduction. 6.2 Method of Percentiles Estimation. 6.3 Method of Moments Estimation. 6.4 Maximum Likelihood Inference. 6.5 Bayesian Inference. 7 Bernoulli Distribution. 7.1 Random Number Generation. 7.2 Curtailed Bernoulli Trial Sequences. 7.3 Urn Sampling Scheme. 7.4 Note. 8 Beta Distribution. 8.1 Notes on Beta and Gamma Functions. 8.2 Variate Relationships. 8.3 Parameter Estimation. 8.4 Random Number Generation. 8.5 Inverted Beta Distribution. 8.6 Noncentral Beta Distribution. 8.7 Beta Binomial Distribution. 9 Binomial Distribution. 9.1 Variate Relationships. 9.2 Parameter Estimation. 9.3 Random Number Generation. 10 Cauchy Distribution. 10.1 Note. 10.2 Variate Relationships. 10.3 Random Number Generation. 10.4 Generalized Form. 11 Chi-Squared Distribution. 11.1 Variate Relationships. 11.2 Random Number Generation. 11.3 Chi Distribution. 12 Chi-Squared (Noncentral) Distribution. 12.1 Variate Relationships. 13 Dirichlet Distribution. 13.1 Variate Relationships. 13.2 Dirichlet Multinomial Distribution. 14 Empirical Distribution Function. 14.1 Estimation from Uncensored Data. 14.2 Estimation from Censored Data. 14.3 Parameter Estimation. 14.4 Example. 14.5 Graphical Method for the Modified Order-Numbers. 14.6 Model Accuracy. 15 Erlang Distribution. 15.1 Variate Relationships. 15.2 Parameter Estimation. 15.3 Random Number Generation. 16 Error Distribution. 16.1 Note. 16.2 Variate Relationships. 17 Exponential Distribution. 17.1 Note. 17.2 Variate Relationships. 17.3 Parameter Estimation. 17.4 Random Number Generation. 18 Exponential Family. 18.1 Members of the Exponential Family. 18.2 Univariate One-Parameter Exponential Family. 18.3 Estimation. 18.4 Generalized Exponential Distributions. 19 Extreme Value (Gumbel) Distribution. 19.1 Note. 19.2 Variate Relationships. 19.3 Parameter Estimation. 19.4 Random Number Generation. 20 F (Variance Ratio) or Fisher{ Snedecor Distribution. 20.1 Variate Relationships. 21 F (Noncentral) Distribution. 21.1 Variate Relationships. 22 Gamma Distribution. 22.1 Variate Relationships. 22.2 Parameter Estimation. 22.3 Random Number Generation. 22.4 Inverted Gamma Distribution. 22.5 Normal Gamma Distribution. 22.6 Generalized Gamma Distribution. 22.6.1 Variate Relationships. 23 Geometric Distribution. 23.1 Notes. 23.2 Variate Relationships. 23.3 Random Number Generation. 24 Hypergeometric Distribution. 24.1 Note. 24.2 Variate Relationships. 24.3 Parameter Estimation. 24.4 Random Number Generation. 24.5 Negative Hypergeometric Distribution. 24.6 Generalized Hypergeometric (Series) Distribution. 25 Inverse Gaussian (Wald) Distribution. 25.1 Variate Relationships. 25.2 Parameter Estimation. 26 Laplace Distribution. 26.1 Variate Relationships. 26.2 Parameter Estimation. 26.3 Random Number Generation. 27 Logarithmic Series Distribution. 27.1 Variate Relationships. 27.2 Parameter Estimation. 28 Logistic Distribution. 28.1 Notes. 28.2 Variate Relationships. 28.3 Parameter Estimation. 28.4 Random Number Generation. 29 Lognormal Distribution. 29.1 Variate Relationships. 29.2 Parameter Estimation. 29.3 Random Number Generation. 30 Multinomial Distribution. 30.1 Variate Relationships. 30.2 Parameter Estimation. 31 Multivariate Normal (Multinormal) Distribution. 31.1 Variate Relationships. 31.2 Parameter Estimation. 32 Negative Binomial Distribution. 32.1 Note. 32.2 Variate Relationships. 32.3 Parameter Estimation. 32.4 Random Number Generation. 33 Normal (Gaussian) Distribution. 33.1 Variate Relationships. 33.2 Parameter Estimation. 33.3 Random Number Generation. 33.4 Truncated Normal Distribution. 33.5 Variate Relationships. 34 Pareto Distribution. 34.1 Note. 34.2 Variate Relationships. 34.3 Parameter Estimation. 34.4 Random Number Generation. 35 Poisson Distribution. 35.1 Note. 35.2 Variate Relationships. 35.3 Parameter Estimation. 35.4 Random Number Generation. 36 Power Function Distribution. 36.1 Variate Relationships. 36.2 Parameter Estimation. 36.3 Random Number Generation. 37 Power Series (Discrete) Distribution. 37.1 Note. 37.2 Variate Relationships. 37.3 Parameter Estimation. 38 Queuing Formulas. 38.1 Characteristics of Queuing Systems. 38.2 Definitions, Notation and Terminology. 38.3 General Formulas. 38.4 Some Standard Queuing Systems. 39 Rayleigh Distribution. 39.1 Variate Relationships. 39.2 Parameter Estimation. 40 Rectangular (Uniform) Continuous Distribution. 40.1Variate Relationships. 40.2 Parameter Estimation. 40.3 Random Number Generation. 41 Rectangular (Uniform) Discrete Distribution. 41.1 General Form. 41.2 Parameter Estimation. 42 Student's t Distribution. 42.1 Variate Relationships. 42.2 Random Number Generation. 43 Student's t (Noncentral) Distribution. 43.1 Variate Relationships. 44 Triangular Distribution. 44.1 Variate Relationships. 44.2 Random Number Generation. 45 von Mises Distribution. 45.1 Note. 45.2 Variate Relationships. 45.3 Parameter Estimation. 46 Weibull Distribution. 46.1 Note. 46.2 Variate Relationships. 46.3 Parameter Estimation. 46.4 Random Number Generation. 46.5 Three-Parameter Weibull Distribution. 46.6Three-Parameter Weibull Random Number Generation. 46.7 Bi-Weibull Distribution. 46.8 Five-Parameter Bi-Weibull Distribution. Bi-Weibull Random Number Generation. Bi-Weibull Graphs. 46.9 Weibull Family. 47 Wishart (Central) Distribution. 47.1 Note. 47.2 Variate Relationships. 48 Statistical Tables. Bibliography.
£63.86
John Wiley & Sons Inc Essential Math Skills for Engineers
Book SynopsisJust the math skills you need to excel in the study or practice of engineering Good math skills are indispensable for all engineers regardless of their specialty, yet only a relatively small portion of the math that engineering students study in college mathematics courses is used on a frequent basis in the study or practice of engineering.Trade Review"Summarizing, this is a very nice textbook, covering many interesting topics and written in a very digestible manner, which can be warmly recommended to students in natural sciences, computer science, and all branches of engineering." (Zentralblatt MATH, 2011)Table of ContentsPreface. 1 What Do Engineers Do? 2 Miscellaneous Math Skills. 2.1 Equations of Lines, Planes, and Circles. 2.2 Areas and Volumes of Common Shapes. 2.3 Roots of a Quadratic Equation. 2.4 Logarithms. 2.5 Reduction of Fractions and Lowest Common Denominators. 2.6 Long Division. 2.7 Trigonometry. 2.7.1 The Common Trigonometric Functions: Sine, Cosine, and Tangent. 2.7.2 Areas of Triangles. 2.7.3 The Hyperbolic Trigonometric Functions: Sinh, Cosh, and Tanh. 2.8 Complex Numbers and Algebra, and Euler’s Identity. 2.8.1 Solution of Differential Equations Having Sinusoidal Forcing Functions. 2.9 Common Derivatives and Their Interpretation. 2.10 Common Integrals and Their Interpretation. 2.11 Numerical Integration. 3 Solution of Simultaneous, Linear, Algebraic Equations. 3.1 How to Identify Simultaneous, Linear, Algebraic Equations. 3.2 The Meaning of a Solution. 3.3 Cramer’s Rule and Symbolic Equations. 3.4 Gauss Elimination. 3.5 Matrix Algebra. 4 Solution of Linear, Constant-Coeffi cient, Ordinary Differential Equations. 4.1 How to Identify Linear, Constant-Coeffi cient, Ordinary Differential Equations. 4.2 Where They Arise: The Meaning of a Solution. 4.3 Solution of First-Order Equations. 4.3.1 The Homogeneous Solution. 4.3.2 The Forced Solution for “Nice” f(t). 4.3.3 The Total Solution. 4.3.4 A Special Case. 4.4 Solution of Second-Order Equations. 4.4.1 The Homogeneous Solution. 4.4.2 The Forced Solution for “Nice” f(t). 4.4.3 The Total Solution. 4.4.4 A Special Case. 4.5 Stability of the Solution. 4.6 Solution of Simultaneous Sets of Ordinary Differential Equations with the Differential Operator. 4.6.1 Using the Differential Operator to Verify Solutions. 4.7 Numerical (Computer) Solutions. 5 Solution of Linear, Constant-Coeffi cient, Difference Equations. 5.1 Where Difference Equations Arise. 5.2 How to Identify Linear, Constant-Coeffi cient Difference Equations. 5.3 Solution of First-Order Equations. 5.3.1 The Homogeneous Solution. 5.3.2 The Forced Solution for “Nice” f(n). 5.3.3 The Total Solution. 5.3.4 A Special Case. 5.4 Solution of Second-Order Equations. 5.4.1 The Homogeneous Solution. 5.4.2 The Forced Solution for “Nice” f(n). 5.4.3 The Total Solution. 5.4.4 A Special Case. 5.5 Stability of the Solution. 5.6 Solution of Simultaneous Sets of Difference Equations with the Difference Operator. 5.6.1 Using the Difference Operator to Verify Solutions. 6 Solution of Linear, Constant-Coeffi cient, Partial Differential Equations. 6.1 Common Engineering Partial Differential Equations. 6.2 The Linear, Constant-Coeffi cient, Partial Differential Equation. 6.3 The Method of Separation of Variables. 6.4 Boundary Conditions and Initial Conditions. 6.5 Numerical (Computer) Solutions via Finite Differences: Conversion to Difference Equations. 7 The Fourier Series and Fourier Transform. 7.1 Periodic Functions. 7.2 The Fourier Series. 7.3 The Fourier Transform. 8 The Laplace Transform. 8.1 Transforms of Important Functions. 8.2 Useful Transform Properties. 8.3 Transforming Differential Equations. 8.4 Obtaining the Inverse Transform Using Partial Fraction Expansions. 9 Mathematics of Vectors. 9.1 Vectors and Coordinate Systems. 9.2 The Line Integral. 9.3 The Surface Integral. 9.4 Divergence. 9.4.1 The Divergence Theorem. 9.5 Curl. 9.5.1 Stokes’ Theorem. 9.6 The Gradient of a Scalar Field. Index.
£46.50
John Wiley & Sons Inc Optimal Learning
Book SynopsisLearn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive.Trade Review“He concludes, "This book collects a number of interesting ideas in optimal learning, allows for connections to be made across disciplines, and is a welcome addition to my bookshelf.” (Informs Journal on Computing, 1 October 2012) Table of ContentsPreface xv Acknowledgments xix 1 The challenges of learning 1 1.1 Learning the best path 2 1.2 Areas of application 4 1.3 Major problem classes 12 1.4 The different types of learning 13 1.5 Learning from different communities 16 1.6 Information collection using decision trees 18 1.6.1 A basic decision tree 18 1.6.2 Decision tree for offline learning 20 1.6.3 Decision tree for online learning 21 1.6.4 Discussion 25 1.7 Website and downloadable software 26 1.8 Goals of this book 26 Problems 28 2 Adaptive learning 31 2.1 The frequentist view 32 2.2 The Bayesian view 33 2.2.1 The updating equations for independent beliefs 34 2.2.2 The expected value of information 36 2.2.3 Updating for correlated normal priors 38 2.2.4 Bayesian updating with an uninformative prior 41 2.3 Updating for non-Gaussian priors 42 2.3.1 The gamma-exponential model 43 2.3.2 The gamma-Poisson model 44 2.3.3 The Pareto-uniform model 45 2.3.4 Models for learning probabilities* 46 2.3.5 Learning an unknown variance* 49 2.4 Monte Carlo simulation 51 2.5 Why does it work?* 54 2.5.1 Derivation of ~_ 54 2.5.2 Derivation of Bayesian updating equations for independent beliefs 55 2.6 Bibliographic notes 57 Problems 57 3 The economics of information 61 3.1 An elementary information problem 61 3.2 The marginal value of information 65 3.3 An information acquisition problem 68 3.4 Bibliographic notes 70 Problems 70 4 Ranking and selection 71 4.1 The model 72 4.2 Measurement policies 75 4.2.1 Deterministic vs. sequential policies 75 4.2.2 Optimal sequential policies 76 4.2.3 Heuristic policies 77 4.3 Evaluating policies 81 4.4 More advanced topics* 83 4.4.1 An alternative representation of the probability space 83 4.4.2 Equivalence of using true means and sample estimates 84 4.5 Bibliographic notes 85 Problems 85 5 The knowledge gradient 89 5.1 The knowledge gradient for independent beliefs 90 5.1.1 Computation 91 5.1.2 Some properties of the knowledge gradient 93 5.1.3 The four distributions of learning 94 5.2 The value of information and the S-curve effect 95 5.3 Knowledge gradient for correlated beliefs 98 5.4 The knowledge gradient for some non-Gaussian distributions 103 5.4.1 The gamma-exponential model 104 5.4.2 The gamma-Poisson model 107 5.4.3 The Pareto-uniform model 108 5.4.4 The beta-Bernoulli model 109 5.4.5 Discussion 111 5.5 Relatives of the knowledge gradient 112 5.5.1 Expected improvement 113 5.5.2 Linear loss* 114 5.6 Other issues 116 5.6.1 Anticipatory vs. experiential learning 117 5.6.2 The problem of priors 118 5.6.3 Discussion 120 5.7 Why does it work?* 121 5.7.1 Derivation of the knowledge gradient formula 121 5.8 Bibliographic notes 125 Problems 126 6 Bandit problems 139 6.1 The theory and practice of Gittins indices 141 6.1.1 Gittins indices in the beta-Bernoulli model 142 6.1.2 Gittins indices in the normal-normal model 145 6.1.3 Approximating Gittins indices 147 6.2 Variations of bandit problems 148 6.3 Upper confidence bounding 149 6.4 The knowledge gradient for bandit problems 151 6.4.1 The basic idea 151 6.4.2 Some experimental comparisons 153 6.4.3 Non-normal models 156 6.5 Bibliographic notes 157 Problems 157 7 Elements of a learning problem 163 7.1 The states of our system 164 7.2 Types of decisions 166 7.3 Exogenous information 167 7.4 Transition functions 168 7.5 Objective functions 168 7.5.1 Designing versus controlling 168 7.5.2 Measurement costs 170 7.5.3 Objectives 170 7.6 Evaluating policies 175 7.7 Discussion 177 7.8 Bibliographic notes 178 Problems 178 8 Linear belief models 181 8.1 Applications 182 8.1.1 Maximizing ad clicks 182 8.1.2 Dynamic pricing 184 8.1.3 Housing loans 184 8.1.4 Optimizing dose response 185 8.2 A brief review of linear regression 186 8.2.1 The normal equations 186 8.2.2 Recursive least squares 187 8.2.3 A Bayesian interpretation 188 8.2.4 Generating a prior 189 8.3 The knowledge gradient for a linear model 191 8.4 Application to drug discovery 192 8.5 Application to dynamic pricing 196 8.6 Bibliographic notes 200 Problems 200 9 Subset selection problems 203 9.1 Applications 205 9.2 Choosing a subset using ranking and selection 206 9.2.1 Setting prior means and variances 207 9.2.2 Two strategies for setting prior covariances 208 9.3 Larger sets 209 9.3.1 Using simulation to reduce the problem size 210 9.3.2 Computational issues 212 9.3.3 Experiments 213 9.4 Very large sets 214 9.5 Bibliographic notes 216 Problems 216 10 Optimizing a scalar function 219 10.1 Deterministic measurements 219 10.2 Stochastic measurements 223 10.2.1 The model 223 10.2.2 Finding the posterior distribution 224 10.2.3 Choosing the measurement 226 10.2.4 Discussion 229 10.3 Bibliographic notes 229 Problems 229 11 Optimal bidding 231 11.1 Modeling customer demand 233 11.1.1 Some valuation models 233 11.1.2 The logit model 234 11.2 Bayesian modeling for dynamic pricing 237 11.2.1 A conjugate prior for choosing between two demand curves 237 11.2.2 Moment matching for non-conjugate problems 239 11.2.3 An approximation for the logit model 242 11.3 Bidding strategies 244 11.3.1 An idea from multi-armed bandits 245 11.3.2 Bayes-greedy bidding 245 11.3.3 Numerical illustrations 247 11.4 Why does it work?* 251 11.4.1 Moment matching for Pareto prior 251 11.4.2 Approximating the logistic expectation 252 11.5 Bibliographic notes 253 Problems 254 12 Stopping problems 255 12.1 Sequential probability ratio test 255 12.2 The secretary problem 260 12.2.1 Setup 261 12.2.2 Solution 263 12.3 Bibliographic notes 266 Problems 266 13 Active learning in statistics 269 13.1 Deterministic policies 270 13.2 Sequential policies for classification 274 13.2.1 Uncertainty sampling 274 13.2.2 Query by committee 275 13.2.3 Expected error reduction 276 13.3 A variance minimizing policy 277 13.4 Mixtures of Gaussians 279 13.4.1 Estimating parameters 280 13.4.2 Active learning 281 13.5 Bibliographic notes 283 14 Simulation optimization 285 14.1 Indifference zone selection 287 14.1.1 Batch procedures 288 14.1.2 Sequential procedures 290 14.1.3 The 0-1 procedure: connection to linear loss 291 14.2 Optimal computing budget allocation 292 14.2.1 Indifference-zone version 293 14.2.2 Linear loss version 294 14.2.3 When does it work? 295 14.3 Model-based simulated annealing 296 14.4 Other areas of simulation optimization 298 14.5 Bibliographic notes 299 15 Learning in mathematical programming 301 15.1 Applications 303 15.1.1 Piloting a hot air balloon 303 15.1.2 Optimizing a portfolio 308 15.1.3 Network problems 309 15.1.4 Discussion 313 15.2 Learning on graphs 313 15.3 Alternative edge selection policies 316 15.4 Learning costs for linear programs* 317 15.5 Bibliographic notes 324 16 Optimizing over continuous measurements 325 16.1 The belief model 327 16.1.1 Updating equations 328 16.1.2 Parameter estimation 330 16.2 Sequential kriging optimization 332 16.3 The knowledge gradient for continuous parameters* 334 16.3.1 Maximizing the knowledge gradient 334 16.3.2 Approximating the knowledge gradient 335 16.3.3 The gradient of the knowledge gradient 336 16.3.4 Maximizing the knowledge gradient 338 16.3.5 The KGCP policy 339 16.4 Efficient global optimization 340 16.5 Experiments 341 16.6 Extension to higher dimensional problems 342 16.7 Bibliographic notes 343 17 Learning with a physical state 345 17.1 Introduction to dynamic programming 347 17.1.1 Approximate dynamic programming 348 17.1.2 The exploration vs. exploitation problem 350 17.1.3 Discussion 351 17.2 Some heuristic learning policies 352 17.3 The local bandit approximation 353 17.4 The knowledge gradient in dynamic programming 355 17.4.1 Generalized learning using basis functions 355 17.4.2 The knowledge gradient 358 17.4.3 Experiments 361 17.5 An expected improvement policy 363 17.6 Bibliographic notes 364 Index 379
£95.36
John Wiley & Sons Inc Greens Functions and Boundary Value Problems
Book SynopsisPraise for the Second Edition This book is an excellent introduction to the wide field of boundary value problems.Journal of Engineering Mathematics No doubt this textbook will be useful for both students and research workers.Mathematical Reviews A new edition of the highly-acclaimed guide to boundary value problems, now featuring modern computational methods and approximation theory Green''s Functions and Boundary Value Problems, Third Edition continues the tradition of the two prior editions by providing mathematical techniques for the use of differential and integral equations to tackle important problems in applied mathematics, the physical sciences, and engineering. This new edition presents mathematical concepts and quantitative tools that are essential for effective use of modern computational methods that play a key role in the practical solution of boundary value problems. With a careful blend of theory and applications, the authors succeTrade Review Table of ContentsPreface to Third Edition. Preface to Second Edition. Preface to First Edition. 0 Preliminaries. 0.1 Heat Conduction. 0.2 Diffusion. 0.3 Reaction-Diffusion Problems. 0.4 The Impulse-Momentum Law: The Motion of Rods and Strings. 0.5 Alternative Formulations of Physical Problems. 0.6 Notes on Convergence. 0.7 The Lebesgue Integral. 1 Green’s Functions (Intuitive Ideas). 1.1 Introduction and General Comments. 1.2 The Finite Rod. 1.3 The Maximum Principle. 1.4 Examples of Green’s Functions. 2 The Theory of Distributions. 2.1 Basic Ideas, Definitions, and Examples. 2.2 Convergence of Sequences and Series of Distributions. 2.3 Fourier Series. 2.4 Fourier Transforms and Integrals. 2.5 Differential Equations in Distributions. 2.6 Weak Derivatives and Sobolev Spaces. 3 One-Dimensional Boundary Value Problems. 3.1 Review. 3.2 Boundary Value Problems for Second-Order Equations. 3.3 Boundary Value Problems for Equations of Order p. 3.4 Alternative Theorems. 3.5 Modified Green's Functions. 4 Hilbert and Banach Spaces. 4.1 Functions and Transformations. 4.2 Linear Spaces. 4.3 Metric Spaces, Normed Linear Spaces, and Banach Spaces. 4.4 Contractions and the Banach Fixed-Point Theorem. 4.5 Hilbert Spaces and the Projection Theorem. 4.6 Separable Hilbert Spaces and Orthonormal Bases. 4.7 Linear Functionals and the Riesz Representation Theorem. 4.8 The Hahn-Banach Theorem and Reflexive Banach Spaces. 5 Operator Theory. 5.1 Basic Ideas and Examples. 5.2 Closed Operators. 5.3 Invertibility: The State of an Operator. 5.4 Adjoint Operators. 5.5 Solvability Conditions. 5.6 The Spectrum of an Operator. 5.7 Compact Operators. 5.8 Extremal Properties of Operators. 5.9 The Banach-Schauder and Banach-Steinhaus Theorems. 6 Integral Equations. 6.1 Introduction. 6.2 Fredholm Integral Equations. 6.3 The Spectrum of a Self-Adjoint Compact Operator. 6.4 The Inhomogeneous Equation. 6.5 Variational Principles and Related Approximation Methods. 7 Spectral Theory of Second-Order Differential Operators. 7.1 Introduction; The Regular Problem. 7.2 Weyl’s Classification of Singular Problems. 7.3 Spectral Problems with a Continuous Spectrum. 8 Partial Differential Equations. 8.1 Classification of Partial Differential Equations. 8.2 Well-Posed Problems for Hyperbolic and Parabolic Equations. 8.3 Elliptic Equations. 8.4 Variational Principles for Inhomogeneous Problems. 8.5 The Lax-Milgram Theorem. 9 Nonlinear Problems. 9.1 Introduction and Basic Fixed-Point Techniques. 9.2 Branching Theory. 9.3 Perturbation Theory for Linear Problems. 9.4 Techniques for Nonlinear Problems. 9.5 The Stability of the Steady State. 10 Approximation Theory and Methods. 10.1 Nonlinear Analysis Tools for Banach Spaces. 10.2 Best and Near-Best Approximation in Banach Spaces. 10.3 Overview of Sobolev and Besov Spaces. 10.4 Applications to Nonlinear Elliptic Equations. 10.5 Finite Element and Related Discretization Methods. 10.6 Iterative Methods for Discretized Linear Equations. 10.7 Methods for Nonlinear Equations. Index.
£118.76
John Wiley & Sons Inc Mathematical Finance
Book SynopsisThis concise book puts the focus on financial problem solving using readily accessible mathematical methods as tools for understanding. Selected formulae are used to illustrate and clarify the underlying logic of problem solving and to provide readers with additional opportunities to enhance their understanding of financial problems.Table of ContentsPreface xv UNIT I MATHEMATICAL INTRODUCTION 1 1 Numbers, Exponents, and Logarithms 3 1.1. Numbers, 3 1.2. Fractions, 3 1.3. Decimals, 5 1.4. Repetends, 6 1.5. Percentages, 7 1.6. Base Amount, Percentage Rate, and Percentage Amount, 8 1.7. Ratios, 9 1.8. Proportions, 10 1.9. Aliquots, 10 1.10. Exponents, 11 1.11. Laws of Exponents, 11 1.12. Exponential Function, 12 1.13. Natural Exponential Function, 13 1.14. Laws of Natural Exponents, 14 1.15. Scientific Notation, 15 1.16. Logarithms, 15 1.17. Laws of Logarithms, 16 1.18. Characteristic, Mantissa, and Antilogarithm, 16 1.19. Logarithmic Function, 18 2 Mathematical Progressions 20 2.1. Arithmetic Progression, 20 2.2. Geometric Progression, 23 2.3. Recursive Progression, 26 2.4. Infinite Geometric Progression, 28 2.5. Growth and Decay Curves, 29 2.6. Growth and Decay Functions with a Natural Logarithmic Base, 34 3 Statistical Measures 35 3.1. Basic Combinatorial Rules and Concepts, 35 3.2. Permutation, 37 3.3. Combination, 40 3.4. Probability, 41 3.5. Mathematical Expectation and Expected Value, 44 3.6. Variance, 46 3.7. Standard Deviation, 48 3.8. Covariance, 49 3.9. Correlation, 50 3.10. Normal Distribution, 52 Unit I Summary 54 List of Formulas 55 Exercises for Unit I 60 UNIT II MATHEMATICS OF THE TIME VALUE OF MONEY 63 Introduction 65 1 Simple Interest 67 1.1. Total Interest, 67 1.2. Rate of Interest, 67 1.3. Term of Maturity, 68 1.4. Current Value, 68 1.5. Future Value, 69 1.6. Finding n and r When the Current and Future Values are Both Known, 69 1.7. Simple Discount, 70 1.8. Calculating the Term in Days, 72 1.9. Ordinary Interest and Exact Interest, 73 1.10. Obtaining Ordinary Interest and Exact Interest in Terms of Each Other, 73 1.11. Focal Date and Equation of Value, 75 1.12. Equivalent Time: Finding an Average due Date, 78 1.13. Partial Payments, 80 1.14. Finding the Simple Interest Rate by the Dollar-Weighted Method, 81 2 Bank Discount 83 2.1. Finding FV Using the Discount Formula, 84 2.2. Finding the Discount Term and the Discount Rate, 84 2.3. Difference between a Simple Discount and a Bank Discount, 85 2.4. Comparing the Discount Rate to the Interest Rate, 87 2.5. Discounting a Promissory Note, 88 2.6. Discounting a Treasury Bill, 90 3 Compound Interest 93 3.1. The Compounding Formula, 94 3.2. Finding the Current Value, 97 3.3. Discount Factor, 98 3.4. Finding the Rate of Compound Interest, 100 3.5. Finding the Compounding Term, 100 3.6. The Rule of 72 and Other Rules, 101 3.7. Effective Interest Rate, 102 3.8. Types of Compounding, 104 3.9. Continuous Compounding, 105 3.10. Equations of Value for a Compound Interest, 106 3.11. Equated Time for a Compound Interest, 108 4 Annuities 110 4.1. Types of Annuities, 110 4.2. Future Value of an Ordinary Annuity, 111 4.3. Current Value of an Ordinary Annuity, 114 4.4. Finding the Payment of an Ordinary Annuity, 116 4.5. Finding the Term of an Ordinary Annuity, 118 4.6. Finding the Interest Rate of an Ordinary Annuity, 120 4.7. Annuity Due: Future and Current Values, 121 4.8. Finding the Payment of an Annuity Due, 123 4.9. Finding the Term of an Annuity Due, 124 4.10. Deferred Annuity, 126 4.11. Future and Current Values of a Deferred Annuity, 127 4.12. Perpetuities, 128 Unit II Summary 130 List of Formulas 132 Exercises for Unit II 138 UNIT III MATHEMATICS OF DEBT AND LEASING 145 1 Credit and Loans 147 1.1. Types of Debt, 147 1.2. Dynamics of Interest–Principal Proportions, 148 1.3. Premature Payoff, 152 1.4. Assessing Interest and Structuring Payments, 154 1.5. Cost of Credit, 158 1.6. Finance Charge and Average Daily Balance, 160 1.7. Credit Limit vs. Debt Limit, 162 2 Mortgage Debt 164 2.1. Analysis of Amortization, 164 2.2. Effects of Interest Rate, Term, and Down Payment on the Monthly Payment, 170 2.3. Graduated Payment Mortgage, 172 2.4. Mortgage Points and the Effective Rate, 176 2.5. Assuming a Mortgage Loan, 176 2.6. Prepayment Penalty on a Mortgage Loan, 177 2.7. Refinancing a Mortgage Loan, 178 2.8. Wraparound and Balloon Payment Loans, 180 2.9. Sinking Funds, 182 2.10. Comparing Amortization to Sinking Fund Methods, 187 3 Leasing 189 3.1. For the Lessee, 189 3.2. For the Lessor, 196 Unit III Summary 198 List of Formulas 199 Exercises for Unit III 202 UNIT IV MATHEMATICS OF CAPITAL BUDGETING AND DEPRECIATION 205 1 Capital Budgeting 207 1.1. Net Present Value, 207 1.2. Internal Rate of Return, 210 1.3. Profitability Index, 212 1.4. Capitalization and Capitalized Cost, 213 1.5. Other Capital Budgeting Methods, 216 2 Depreciation and Depletion 219 2.1. The Straight-Line Method, 220 2.2. The Fixed-Proportion Method, 223 2.3. The Sum-of-Digits Method, 226 2.4. The Amortization Method, 229 2.5. The Sinking Fund Method, 231 2.6. Composite Rate and Composite Life, 233 2.7. Depletion, 235 Unit IV Summary 239 List of Formulas 240 Exercises for Unit IV 243 UNIT V MATHEMATICS OF THE BREAK-EVEN POINT AND LEVERAGE 247 1 Break-Even Analysis 249 1.1. Deriving BEQ and BER, 249 1.2. BEQ and BER Variables, 251 1.3. Cash Break-Even Technique, 254 1.4. The Break-even Point and the Target Profit, 256 1.5. Algebraic Approach to the Break-Even Point, 257 1.6. The Break-Even Point When Borrowing, 261 1.7. Dual Break-Even Points, 264 1.8. Other Applications of the Break-Even Point, 267 1.9. BEQ and BER Sensitivity to their Variables, 272 1.10. Uses and Limitations of Break-Even Analysis, 272 2 Leverage 274 2.1. Operating Leverage, 274 2.2. Operating Leverage, Fixed Cost, and Business Risk, 277 2.3. Financial Leverage, 278 2.4. Total or Combined Leverage, 284 Unit V Summary 287 List of Formulas 289 Exercises for Unit V 291 UNIT VI MATHEMATICS OF INVESTMENT 295 1 Stocks 297 1.1. Buying and Selling Stocks, 298 1.2. Common Stock Valuation, 300 1.3. Cost of New Issues of Common Stock, 306 1.4. Stock Value with Two-Stage Dividend Growth, 307 1.5. Cost of Stock through the CAPM, 307 1.6. Other Methods for Common Stock Valuation, 308 1.7. Valuation of Preferred Stock, 309 1.8. Cost of Preferred Stock, 310 2 Bonds 311 2.1. Bond Valuation, 311 2.2. Premium and Discount Prices, 315 2.3. Premium Amortization, 317 2.4. Discount Accumulation, 319 2.5. Bond Purchase Price Between Interest Days, 321 2.6. Estimating the Yield Rate, 324 2.7. Duration, 328 3 Mutual Funds 330 3.1. Fund Evaluation, 331 3.2. Loads, 332 3.3. Performance Measures, 332 3.4. The Effect of Systematic Risk (β), 338 3.5. Dollar-Cost Averaging, 340 4 Options 341 4.1. Dynamics of Making Profits with Options, 343 4.2. Intrinsic Value of Calls and Puts, 344 4.3. Time Value of Calls and Puts, 347 4.4. The Delta Ratio, 348 4.5. Determinants of Option Value, 350 4.6. Option Valuation, 351 4.7. Combined Intrinsic Values of Options, 353 5 Cost of Capital and Ratio Analysis 357 5.1. Before- and After-Tax Cost of Capital, 357 5.2. Weighted-Average Cost of Capital, 358 5.3. Ratio Analysis, 359 5.4. The DuPont Model, 374 5.5. A Final Word about Ratios, 376 Unit VI Summary 377 List of Formulas 379 Exercises for Unit VI 384 UNIT VII MATHEMATICS OF RETURN AND RISK 387 1 Measuring Return and Risk 389 1.1. Expected Rate of Return, 389 1.2. Measuring the Risk, 390 1.3. Risk Aversion and Risk Premium, 394 1.4. Return and Risk at the Portfolio Level, 394 1.5. Markowitz’s Two-Asset Portfolio, 405 1.6. Lending and Borrowing at a Risk-Free Rate of Return, 408 1.7. Types of Risk, 409 2 The Capital Asset Pricing Model (CAPM) 411 2.1. The Financial Beta (β), 411 2.2. The CAPM Equation, 414 2.3. The Security Market Line, 416 2.4. SML Swing by Risk Aversion, 418 Unit VII Summary 422 List of Formulas 423 Exercises for Unit VII 425 UNIT VIII MATHEMATICS OF INSURANCE 429 1 Life Annuities 431 1.1. Mortality Table, 431 1.2. Commutation Terms, 436 1.3. Pure Endowment, 438 1.4. Types of Life Annuities, 439 2 Life Insurance 448 2.1. Whole Life Insurance Policy, 448 2.2. Annual Premium: Whole Life Basis, 449 2.3. Annual Premium: m-Payment Basis, 450 2.4. Deferred Whole Life Policy, 451 2.5. Deferred Annual Premium: Whole Life Basis, 452 2.6. Deferred Annual Premium: m-Payment Basis, 453 2.7. Term Life Insurance Policy, 454 2.8. Endowment Insurance Policy, 456 2.9. Annual Premium for the Endowment Policy, 457 2.10. Less than Annual Premiums, 458 2.11. Natural Premium vs. the Level Premium, 459 2.12. Reserve and Terminal Reserve Funds, 461 2.13. Benefits of the Terminal Reserve, 465 2.14. How Much Life Insurance Should You Buy?, 465 3 Property and Casualty Insurance 470 3.1. Deductibles and Co-Insurance, 472 3.2. Health Care Insurance, 473 3.3. Policy Limit, 476 Unit VIII Summary 477 List of Formulas 478 Exercises for Unit VIII 482 References 485 Appendix 487 Index 515
£95.36
John Wiley & Sons Inc The Mathematics of Derivatives Securities with
Book SynopsisDiscusses analytical issues and intricate financial instruments in a way that it is accessible to postgraduate students with or without a previous background in probability theory and finance. This title covers an overview of MATLAB and the various components that will be used alongside it throughout the textbook.Trade Review“The book can be warmly recommended to readers who wish to learn the main methods of quantitative finance without delving into its mathematical foundations.” (Zentralblatt MATH, 1 December 2012) Table of ContentsPreface xi 1 An Introduction to Probability Theory 1 1.1 The Notion of a Set and a Sample Space 1 1.2 Sigma Algebras or Field 2 1.3 Probability Measure and Probability Space 2 1.4 Measurable Mapping 3 1.5 Cumulative Distribution Functions 4 1.6 Convergence in Distribution 5 1.7 Random Variables 5 1.8 Discrete Random Variables 6 1.9 Example of Discrete Random Variables: The Binomial Distribution 6 1.10 Hypergeometric Distribution 7 1.11 Poisson Distribution 8 1.12 Continuous Random Variables 9 1.13 Uniform Distribution 9 1.14 The Normal Distribution 9 1.15 Change of Variable 11 1.16 Exponential Distribution 12 1.17 Gamma Distribution 12 1.18 Measurable Function 13 1.19 Cumulative Distribution Function and Probability Density Function 13 1.20 Joint, Conditional and Marginal Distributions 17 1.21 Expected Values of Random Variables and Moments of a Distribution 19 2 Stochastic Processes 25 2.1 Stochastic Processes 25 2.2 Martingales Processes 26 2.3 Brownian Motions 29 2.4 Brownian Motion and the Reflection Principle 32 2.5 Geometric Brownian Motions 35 3 Ito Calculus and Ito Integral 37 3.1 Total Variation and Quadratic Variation of Differentiable Functions 37 3.2 Quadratic Variation of Brownian Motions 39 3.3 The Construction of the Ito Integral 40 3.4 Properties of the Ito Integral 41 3.5 The General Ito Stochastic Integral 42 3.6 Properties of the General Ito Integral 43 3.7 Construction of the Ito Integral with Respect to Semi-Martingale Integrators 44 3.8 Quadratic Variation of a General Bounded Martingale 46 4 The Black and Scholes Economy 55 4.1 Introduction 55 4.2 Trading Strategies and Martingale Processes 55 4.3 The Fundamental Theorem of Asset Pricing 56 4.4 Martingale Measures 58 4.5 Girsanov Theorem 59 4.6 Risk-Neutral Measures 62 5 The Black and Scholes Model 67 5.1 Introduction 67 5.2 The Black and Scholes Model 67 5.3 The Black and Scholes Formula 68 5.4 Black and Scholes in Practice 70 5.5 The Feynman–Kac Formula 71 6 Monte Carlo Methods 79 6.1 Introduction 79 6.2 The Data Generating Process (DGP) and the Model 79 6.3 Pricing European Options 80 6.4 Variance Reduction Techniques 81 7 Monte Carlo Methods and American Options 91 7.1 Introduction 91 7.2 Pricing American Options 91 7.3 Dynamic Programming Approach and American Option Pricing 92 7.4 The Longstaff and Schwartz Least Squares Method 93 7.5 The Glasserman and Yu Regression Later Method 95 7.6 Upper and Lower Bounds and American Options 96 8 American Option Pricing: The Dual Approach 101 8.1 Introduction 101 8.2 A General Framework for American Option Pricing 101 8.3 A Simple Approach to Designing Optimal Martingales 104 8.4 Optimal Martingales and American Option Pricing 104 8.5 A Simple Algorithm for American Option Pricing 105 8.6 Empirical Results 106 8.7 Computing Upper Bounds 107 8.8 Empirical Results 109 9 Estimation of Greeks using Monte Carlo Methods 113 9.1 Finite Difference Approximations 113 9.2 Pathwise Derivatives Estimation 114 9.3 Likelihood Ratio Method 116 9.4 Discussion 118 10 Exotic Options 121 10.1 Introduction 121 10.2 Digital Options 121 10.3 Asian Options 122 10.4 Forward Start Options 123 10.5 Barrier Options 123 10.5.1 Hedging Barrier Options 125 11 Pricing and Hedging Exotic Options 129 11.1 Introduction 129 11.2 Monte Carlo Simulations and Asian Options 129 11.3 Simulation of Greeks for Exotic Options 130 11.4 Monte Carlo Simulations and Forward Start Options 131 11.5 Simulation of the Greeks for Exotic Options 132 11.6 Monte Carlo Simulations and Barrier Options 132 12 Stochastic Volatility Models 137 12.1 Introduction 137 12.2 The Model 137 12.3 Square Root Diffusion Process 138 12.4 The Heston Stochastic Volatility Model (HSVM) 139 12.5 Processes with Jumps 143 12.6 Application of the Euler Method to Solve SDEs 143 12.7 Exact Simulation Under SV 144 12.8 Exact Simulation of Greeks Under SV 146 13 Implied Volatility Models 151 13.1 Introduction 151 13.2 Modelling Implied Volatility 152 13.3 Examples 153 14 Local Volatility Models 157 14.1 An Overview 157 14.2 The Model 159 14.3 Numerical Methods 161 15 An Introduction to Interest Rate Modelling 167 15.1 A General Framework 167 15.2 Affine Models (AMs) 169 15.3 The Vasicek Model 171 15.4 The Cox, Ingersoll and Ross (CIR) Model 173 15.5 The Hull and White (HW) Model 174 15.6 The Black Formula and Bond Options 175 16 Interest Rate Modelling 177 16.1 Some Preliminary Definitions 177 16.2 Interest Rate Caplets and Floorlets 178 16.3 Forward Rates and Numeraire 180 16.4 Libor Futures Contracts 181 16.5 Martingale Measure 183 17 Binomial and Finite Difference Methods 185 17.1 The Binomial Model 185 17.2 Expected Value and Variance in the Black and Scholes and Binomial Models 186 17.3 The Cox–Ross–Rubinstein Model 187 17.4 Finite Difference Methods 188 Appendix 1 An Introduction to MATLAB 191 A1.1 What is MATLAB? 191 A1.2 Starting MATLAB 191 A1.3 Main Operations in MATLAB 192 A1.4 Vectors and Matrices 192 A1.5 Basic Matrix Operations 194 A1.6 Linear Algebra 195 A1.7 Basics of Polynomial Evaluations 196 A1.8 Graphing in MATLAB 196 A1.9 Several Graphs on One Plot 197 A1.10 Programming in MATLAB: Basic Loops 199 A1.11 M-File Functions 200 A1.12 MATLAB Applications in Risk Management 200 A1.13 MATLAB Programming: Application in Financial Economics 202 Appendix 2 Mortgage Backed Securities 205 A2.1 Introduction 205 A2.2 The Mortgage Industry 206 A2.3 The Mortgage Backed Security (MBS) Model 207 A2.4 The Term Structure Model 208 A2.5 Preliminary Numerical Example 210 A2.6 Dynamic Option Adjusted Spread 210 A2.7 Numerical Example 212 A2.8 Practical Numerical Examples 213 A2.9 Empirical Results 214 A2.10 The Pre-Payment Model 215 Appendix 3 Value at Risk 217 A3.1 Introduction 217 A3.2 Value at Risk (VaR) 217 A3.3 The Main Parameters of a VaR 218 A3.4 VaR Methodology 219 A3.5 Empirical Applications 222 A3.6 Fat Tails and VaR 224 Bibliography 227 References 229 Index 233
£40.38
John Wiley & Sons Inc How to be a Quantitative Ecologist
Book SynopsisHow to Be a Quantitative Ecologist is comprised of two equal parts on mathematics and statistics with emphasis on quantitative skills. A major component of this guide is computer implementation techniques, accompanied by computer practicals using the language R.Trade Review“For those looking through R books for something a bit more technical, this book will be an essential accomplice to mastering R.” (British Ecological Society, 1 April 2013) “The book is written in a style that is easy to read and for which one quickly forgets that the examples are essentially mathematical in nature. If you are an ecologist who has shied away from quantitative ecology in the past then this may be the text to convince you that there is much to be learnt from quantitative ecology. I thoroughly recommend this book and trust that you enjoy reading it as much as I did.” (International Statistical Review, 2012) "After a course of one or two semesters using this textbook, he says, students should have the absolute minimum of knowledge about quantitative research that ecologists need, but can provide a foundation for students who want to move further in that direction." (Book News, 1 August 2011) Table of ContentsHow I chose to write this book, and why you might choose to read it. Preface. 0. How to start a meaningful relationship with your computer. Introduction to R. 0.1 What is R? 0.2 Why use R for this book? 0.3 Computing with a scientific package like R. 0.4 Installing and interacting with R. 0.5 Style conventions. 0.6 Valuable R accessories. 0.7 Getting help. 0.8 Basic R usage. 0.9 Importing data from a spreadsheet. 0.10 Storing data in data frames. 0.11 Exporting data from R. 0.12 Quitting R. 1. How to make mathematical statements. Numbers, equations and functions. 1.1 Qualitative and quantitative scales. 1.2 Numbers. 1.3 Symbols. 1.4 Logical operations. 1.5 Algebraic operations. 1.6 Manipulating numbers. 1.7 Manipulating units. 1.8 Manipulating expressions. 1.9 Polynomials. 1.10 Equations. 1.11 First order polynomial equations. 1.12 Proportionality and scaling: a special kind of first order polynomial equation. 1.13 Second and higher order polynomial equations. 1.14 Systems of polynomial equations. 1.15 Inequalities. 1.16 Coordinate systems. 1.17 Complex numbers. 1.18 Relations and functions. 1.19 The graph of a function. 1.20 First order polynomial functions. 1.21 Higher order polynomial functions. 1.22 The relationship between equations and functions. 1.23 Other useful functions. 1.24 Inverse functions. 1.25 Functions of more than one variable. 2. How to describe regular shapes and patterns. Geometry and trigonometry. 2.1 Primitive elements. 2.2 Axioms of Euclidean geometry. 2.3 Propositions. 2.4 Distance between two points. 2.5 Areas and volumes. 2.6 Measuring angles. 2.7 The trigonometric circle. 2.8 Trigonometric functions. 2.9 Polar coordinates. 2.10 Graphs of trigonometric functions. 2.11 Trigonometric identities. 2.12 Inverses of trigonometric functions. 2.13 Trigonometric equations. 2.14 Modifying the basic trigonometric graphs. 2.15 Superimposing trigonometric functions. 2.16 Spectral analysis. 2.17 Fractal geometry. 3. How to change things, one step at a time. Sequences, difference equations and logarithms. 3.1 Sequences. 3.2 Difference equations. 3.3 Higher order difference equations. 3.4 Initial conditions and parameters. 3.5 Solutions of a difference equation. 3.6 Equilibrium solutions. 3.7 Stable and unstable equilibria. 3.8 Investigating stability. 3.9 Chaos. 3.10 Exponential function. 3.11 Logarithmic function. 3.12 Logarithmic equations. 4. How to change things, continuously. Derivatives and their applications. 4.1 Average rate of change. 4.2 Instantaneous rate of change. 4.3 Limits. 4.4 The derivative of a function. 4.5 Differentiating polynomials. 4.6 Differentiating other functions. 4.7 The chain rule. 4.8 Higher order derivatives. 4.9 Derivatives of functions of many variables. 4.10 Optimisation. 4.11 Local stability for difference equations. 4.12 Series expansions. 5. How to work with accumulated change. Integrals and their applications. 5.1 Antiderivatives. 5.2 Indefinite integrals. 5.3 Three analytical methods of integration. 5.4 Summation. 5.5 Area under a curve. 5.6 Definite integrals. 5.7 Some properties of definite integrals. 5.8 Improper integrals. 5.9 Differential equations. 5.10 Solving differential equations. 5.11 Stability analysis for differential equations. 6. How to keep stuff organised in tables. Matrices and their applications. 6.1 Matrices. 6.2 Matrix operations. 6.3 Geometric interpretation of vectors and square matrices. 6.4 Solving systems of equations with matrices. 6.5 Markov chains. 6.6 Eigenvalues and eigenvectors. 6.7 Leslie matrix models. 6.8 Analysis of linear dynamical systems. 6.9 Analysis of nonlinear dynamical systems. 7. How to visualise and summarise data. Descriptive statistics. 7.1 Overview of statistics. 7.2 Statistical variables. 7.3 Populations and samples. 7.4 Single-variable samples. 7.5 Frequency distributions. 7.6 Measures of centrality. 7.7 Measures of spread. 7.8 Skewness and kurtosis. 7.9 Graphical summaries. 7.10 Data sets with more than one variable. 7.11 Association between qualitative variables. 7.12 Association between quantitative variables. 7.13 Joint frequency distributions. 8. How to put a value on uncertainty. Probability. 8.1 Random experiments and event spaces. 8.2 Events. 8.3 Frequentist probability. 8.4 Equally likely events. 8.5 The union of events. 8.6 Conditional probability. 8.7 Independent events. 8.8 Total probability. 8.9 Bayesian probability. 9. How to identify different kinds of randomness. Probability distributions. 9.1 Probability distributions. 9.2 Discrete probability distributions. 9.3 Continuous probability distributions. 9.4 Expectation. 9.5 Named distributions. 9.6 Equally likely events: the uniform distribution. 9.7 Hit or miss: the Bernoulli distribution. 9.8 Count of occurrences in a given number of trials: the binomial distribution. 9.9 Counting different types of occurrences: the multinomial distribution. 9.10 Number of occurrences in a unit of time or space: the Poisson distribution. 9.11 The gentle art of waiting: geometric, negative binomial, exponential and gamma distributions. 9.12 Assigning probabilities to probabilities: the beta and Dirichlet distributions. 9.13 Perfect symmetry: the normal distribution. 9.14 Because it looks right: using probability distributions empirically. 9.15 Mixtures, outliers and the t-distribution. 9.16 Joint, conditional and marginal probability distributions. 9.17 The bivariate normal distribution. 9.18 Sums of random variables: the central limit theorem. 9.19 Products of random variables: the log-normal distribution. 9.20 Modelling residuals: the chi-square distribution. 9.21 Stochastic simulation. 10. How to see the forest from the trees. Estimation and testing. 10.1 Estimators and their properties. 10.2 Normal theory. 10.3 Estimating the population mean. 10.4 Estimating the variance of a normal population. 10.5 Confidence intervals. 10.6 Inference by bootstrapping. 10.7 More general estimation methods. 10.8 Estimation by least squares. 10.9 Estimation by maximum likelihood. 10.10 Bayesian estimation. 10.11 Link between maximum likelihood and Bayesian estimation. 10.12 Hypothesis testing: rationale. 10.13 Tests for the population mean. 10.14 Tests comparing two different means. 10.15 Hypotheses about qualitative data. 10.16 Hypothesis testing debunked. 11. How to separate the signal from the noise. Statistical modelling. 11.1 Comparing the means of several populations. 11.2 Simple linear regression. 11.3 Prediction. 11.4 How good is the best-fit line? 11.5 Multiple linear regression. 11.6 Model selection. 11.7 Generalised linear models. 11.8 Evaluation, diagnostics and model selection for GLMs. 11.9 Modelling dispersion. 11.10 Fitting more complicated models to data: polynomials, interactions, nonlinear regression. 11.11 Letting the data suggest more complicated models: smoothing. 11.12 Partitioning variation: mixed effects models. 12. How to measure similarity. Multivariate methods 12.1 The problem with multivariate data. 12.2 Ordination in general. 12.3 Principal components analysis. 12.4 Clustering in general. 12.5 Agglomerative hierarchical clustering. 12.6 Nonhierarchical clustering: k means analysis. 12.7 Classification in general. 12.8 Logistic regression: two classes. 12.9 Logistic regression: many classes. Further reading. References. Appendix: Formulae. R Index. Index.
£107.06
John Wiley & Sons Inc How to be a Quantitative Ecologist
Book SynopsisHow to Be a Quantitative Ecologist is comprised of two equal parts on mathematics and statistics with emphasis on quantitative skills. A major component of this guide is computer implementation techniques, accompanied by computer practicals using the language R.Trade Review“For those looking through R books for something a bit more technical, this book will be an essential accomplice to mastering R.” (British Ecological Society, 1 April 2013) “The book is written in a style that is easy to read and for which one quickly forgets that the examples are essentially mathematical in nature. If you are an ecologist who has shied away from quantitative ecology in the past then this may be the text to convince you that there is much to be learnt from quantitative ecology. I thoroughly recommend this book and trust that you enjoy reading it as much as I did.” (International Statistical Review, 2012) "After a course of one or two semesters using this textbook, he says, students should have the absolute minimum of knowledge about quantitative research that ecologists need, but can provide a foundation for students who want to move further in that direction." (Book News, 1 August 2011) Table of ContentsHow I chose to write this book, and why you might choose to read it. Preface. 0. How to start a meaningful relationship with your computer. Introduction to R. 0.1 What is R? 0.2 Why use R for this book? 0.3 Computing with a scientific package like R. 0.4 Installing and interacting with R. 0.5 Style conventions. 0.6 Valuable R accessories. 0.7 Getting help. 0.8 Basic R usage. 0.9 Importing data from a spreadsheet. 0.10 Storing data in data frames. 0.11 Exporting data from R. 0.12 Quitting R. 1. How to make mathematical statements. Numbers, equations and functions. 1.1 Qualitative and quantitative scales. 1.2 Numbers. 1.3 Symbols. 1.4 Logical operations. 1.5 Algebraic operations. 1.6 Manipulating numbers. 1.7 Manipulating units. 1.8 Manipulating expressions. 1.9 Polynomials. 1.10 Equations. 1.11 First order polynomial equations. 1.12 Proportionality and scaling: a special kind of first order polynomial equation. 1.13 Second and higher order polynomial equations. 1.14 Systems of polynomial equations. 1.15 Inequalities. 1.16 Coordinate systems. 1.17 Complex numbers. 1.18 Relations and functions. 1.19 The graph of a function. 1.20 First order polynomial functions. 1.21 Higher order polynomial functions. 1.22 The relationship between equations and functions. 1.23 Other useful functions. 1.24 Inverse functions. 1.25 Functions of more than one variable. 2. How to describe regular shapes and patterns. Geometry and trigonometry. 2.1 Primitive elements. 2.2 Axioms of Euclidean geometry. 2.3 Propositions. 2.4 Distance between two points. 2.5 Areas and volumes. 2.6 Measuring angles. 2.7 The trigonometric circle. 2.8 Trigonometric functions. 2.9 Polar coordinates. 2.10 Graphs of trigonometric functions. 2.11 Trigonometric identities. 2.12 Inverses of trigonometric functions. 2.13 Trigonometric equations. 2.14 Modifying the basic trigonometric graphs. 2.15 Superimposing trigonometric functions. 2.16 Spectral analysis. 2.17 Fractal geometry. 3. How to change things, one step at a time. Sequences, difference equations and logarithms. 3.1 Sequences. 3.2 Difference equations. 3.3 Higher order difference equations. 3.4 Initial conditions and parameters. 3.5 Solutions of a difference equation. 3.6 Equilibrium solutions. 3.7 Stable and unstable equilibria. 3.8 Investigating stability. 3.9 Chaos. 3.10 Exponential function. 3.11 Logarithmic function. 3.12 Logarithmic equations. 4. How to change things, continuously. Derivatives and their applications. 4.1 Average rate of change. 4.2 Instantaneous rate of change. 4.3 Limits. 4.4 The derivative of a function. 4.5 Differentiating polynomials. 4.6 Differentiating other functions. 4.7 The chain rule. 4.8 Higher order derivatives. 4.9 Derivatives of functions of many variables. 4.10 Optimisation. 4.11 Local stability for difference equations. 4.12 Series expansions. 5. How to work with accumulated change. Integrals and their applications. 5.1 Antiderivatives. 5.2 Indefinite integrals. 5.3 Three analytical methods of integration. 5.4 Summation. 5.5 Area under a curve. 5.6 Definite integrals. 5.7 Some properties of definite integrals. 5.8 Improper integrals. 5.9 Differential equations. 5.10 Solving differential equations. 5.11 Stability analysis for differential equations. 6. How to keep stuff organised in tables. Matrices and their applications. 6.1 Matrices. 6.2 Matrix operations. 6.3 Geometric interpretation of vectors and square matrices. 6.4 Solving systems of equations with matrices. 6.5 Markov chains. 6.6 Eigenvalues and eigenvectors. 6.7 Leslie matrix models. 6.8 Analysis of linear dynamical systems. 6.9 Analysis of nonlinear dynamical systems. 7. How to visualise and summarise data. Descriptive statistics. 7.1 Overview of statistics. 7.2 Statistical variables. 7.3 Populations and samples. 7.4 Single-variable samples. 7.5 Frequency distributions. 7.6 Measures of centrality. 7.7 Measures of spread. 7.8 Skewness and kurtosis. 7.9 Graphical summaries. 7.10 Data sets with more than one variable. 7.11 Association between qualitative variables. 7.12 Association between quantitative variables. 7.13 Joint frequency distributions. 8. How to put a value on uncertainty. Probability. 8.1 Random experiments and event spaces. 8.2 Events. 8.3 Frequentist probability. 8.4 Equally likely events. 8.5 The union of events. 8.6 Conditional probability. 8.7 Independent events. 8.8 Total probability. 8.9 Bayesian probability. 9. How to identify different kinds of randomness. Probability distributions. 9.1 Probability distributions 300 9.2 Discrete probability distributions 301 9.3 Continuous probability distributions 304 9.4 Expectation 306 9.5 Named distributions 309 9.6 Equally likely events: the uniform distribution. 9.7 Hit or miss: the Bernoulli distribution. 9.8 Count of occurrences in a given number of trials: the binomial distribution. 9.9 Counting different types of occurrences: the multinomial distribution. 9.10 Number of occurrences in a unit of time or space: the Poisson distribution. 9.11 The gentle art of waiting: geometric, negative binomial, exponential and gamma distributions. 9.12 Assigning probabilities to probabilities: the beta and Dirichlet distributions. 9.13 Perfect symmetry: the normal distribution. 9.14 Because it looks right: using probability distributions empirically. 9.15 Mixtures, outliers and the t-distribution. 9.16 Joint, conditional and marginal probability distributions. 9.17 The bivariate normal distribution. 9.18 Sums of random variables: the central limit theorem. 9.19 Products of random variables: the log-normal distribution. 9.20 Modelling residuals: the chi-square distribution. 9.21 Stochastic simulation. 10. How to see the forest from the trees. Estimation and testing. 10.1 Estimators and their properties. 10.2 Normal theory. 10.3 Estimating the population mean. 10.4 Estimating the variance of a normal population. 10.5 Confidence intervals. 10.6 Inference by bootstrapping. 10.7 More general estimation methods. 10.8 Estimation by least squares. 10.9 Estimation by maximum likelihood. 10.10 Bayesian estimation. 10.11 Link between maximum likelihood and Bayesian estimation. 10.12 Hypothesis testing: rationale. 10.13 Tests for the population mean. 10.14 Tests comparing two different means. 10.15 Hypotheses about qualitative data. 10.16 Hypothesis testing debunked. 11. How to separate the signal from the noise. Statistical modelling. 11.1 Comparing the means of several populations. 11.2 Simple linear regression. 11.3 Prediction. 11.4 How good is the best-fit line? 11.5 Multiple linear regression. 11.6 Model selection. 11.7 Generalised linear models. 11.8 Evaluation, diagnostics and model selection for GLMs. 11.9 Modelling dispersion 409 11.10 Fitting more complicated models to data: polynomials, interactions, nonlinear regression. 11.11 Letting the data suggest more complicated models: smoothing. 11.12 Partitioning variation: mixed effects models. 12. How to measure similarity. Multivariate methods 12.1 The problem with multivariate data. 12.2 Ordination in general. 12.3 Principal components analysis. 12.4 Clustering in general. 12.5 Agglomerative hierarchical clustering. 12.6 Nonhierarchical clustering: k means analysis. 12.7 Classification in general. 12.8 Logistic regression: two classes. 12.9 Logistic regression: many classes. Further reading. References. Appendix: Formulae. R Index. Index.
£40.80
John Wiley & Sons Inc Levy Processes in Finance Pricing Financial
Book SynopsisFinancial mathematics has recently enjoyed considerable interest on account of its impact on the finance industry. In parallel, the theory of Levy processes has also seen many exciting developments. These powerful modelling tools allow the user to model more complex phenomena, and are commonly applied to problems in finance.Table of ContentsPreface. Acknowledgements. Introduction. Financial Mathematics in Continuous Time. The Black-Scholes Model. Imperfections of the Black-Scholes Model. Lévy Processes and OU Processes. Stock Price Models Driven by Lévy Processes. Lévy Models with Stochastic Volatility. Simulation Techniques. Exotic Option Pricing. Interest-Rate Models. Appendix A: Special Functions. Appendix B: Lévy Processes. Appendix C: S&P 500 Call Option Prices. References. Index.
£107.06
John Wiley & Sons Inc Linear Programming
Book SynopsisA comprehensive, up-to-date text on linear programming. Covers all practical modeling, mathematical, geometrical, algorithmic, and computational aspects. Surveys recent developments in the field, including the Ellipsoid method. Includes extensive examples and exercises.Table of ContentsFormulation of Linear Programs. The Simplex Method. The Geometry of the Simplex Method. Duality in Linear Programming. Revised (Primal) Simplex Method. The Dual Simplex Method. Numerically Stable Forms of the Simplex Method. Parametric Linear Programs. Sensitivity Analysis. Degeneracy in Linear Programming. Bounded-Variable Linear Programs. The Decomposition Principle of Linear Programming. The Transportation Problem. Computational Complexity of the Simplex Algorithm. The Ellipsoid Method. Iterative Methods for Linear Inequalities and Linear Programs. Vector Minima. Index.
£206.06
John Wiley & Sons Inc Sampling Techniques for Forest Resource Inventory
Book SynopsisClearly explains the sampling methods associated with the inventory of forest resources. It avoids extensive coverage of theoretical statistics and mathematics in favor of thorough coverage of forest inventory topics for the practitioner.Table of ContentsA Review of Necessary Statistics and Notation. Elementary Sampling Methods: Selective, Simple Random, andSystematic Sampling. Horizontal Point Sampling. Stratified Random Sampling. Ratio and Regression Estimators. Double Sampling or Two-Phase Sampling. Multistage Sampling--Inventorying Large Acreages. Inventory Methods for Estimating Stand Growth. 3P and Line Intersect Sampling. Estimating Wildlife Population Sizes. Answers to Assorted Problems for Better Understanding. Index.
£174.56
John Wiley & Sons Inc Applied Survival Analysis
Book SynopsisIntroduces applied research areas and a number of real-life questions and examples with basic methods in nonparametric statistics, including the concept of censoring, which distinguishes survival analysis from other areas of statistics.Table of ContentsBasic Concepts in Survival Analysis. Estimation of Functions and Parameters. Comparison of Survival Distributions. Correlation and Regression Analyses. Appendices. References. Index.
£121.46
John Wiley & Sons Inc Rational Choice and Judgment Decision Analysis
Book Synopsis* Unlike the competition this book is problem rather than tool oriented* Provides simple a set of simple systematic heuristic methods for a general course in decision making* Accompanied by an Instructor's Guide. Contact rexvbrown@aol. com. .Trade Review"...the most thorough and accessible treatment of decision analysis that I am familiar with...a comprehensive toolkit that will be useful to anyone who seeks further practice in using the technology." (PscyCRITIQUES, July 19, 2006) "…a well-written textbook aimed at helping students make better personal and professional decisions…the techniques in the book are worthy of study." (MAA Reviews, April 8, 2006) "...the book presents an insight on the practical application of decision analysis in the private and public sector." (EADM Bulletin, Autumn 2005) "...an excellent resource for any organization or as a textbook for decision-making courses in a variety of fields, including public policy, business management, and systems engineering." (SirReadaLot.org)Table of ContentsPreface. Who Might Use This Book. Background. Substance of this Book. Pedagogy. Demands on Students and Instructor. Other Approaches, Other Materials. Author Background. Acknowledgments. Prolog: A Baby Delivery Dilemma. 1. Basics and Overview. 2. Uses of Decision Analysis. Appendix 2A: Decision Analysis Reflects on His Work. 3. Evaluating a Choice Qualitatively. 4. Quantitative Aid to Rational Choice. Appendix 4A: Business Decision Tree Example. Appendix 4B: Private Example: Study or Play? 5. Describing Outcomes. 6. Taking Preference Into Account. 7. Choice Under Uncertainty. Appendix 7A. Technical Notes. 8. Decision Aiding Strategy. Appendix 8A. Influence Sketches. 9. Aiding the Professional Decider. Appendix 9A. Environmental Regulation Case Study. 10. Assessing and Inferring Probabilities. 11. Eliciting Preferences. 12. Applied Term Project. Appendix 12A. Student Project Report. Epilog. References. Glossary of Concepts and Terms. Index.
£99.86
John Wiley & Sons Inc Average Case Analysis of Algorithms on Sequences
Book SynopsisA timely book on a topic that has witnessed a surge of interest over the last decade, owing in part to several novel applications, most notably in data compression and computational molecular biology. It describes methods employed in average case analysis of algorithms, combining both analytical and probabilistic tools in a single volume.Trade Review"Surveying the major techniques of average case analysis, this graduate textbook presents both analytical methods used for well-structured algorithms and probabilistic methods used for more structurally complex algorithms." (SciTech Book News, Vol. 25, No. 3, September 2001) "...contains a comprehensive treatment on probabilistic, combinatorial, and analytical techniques and methods...treatment is clear, rigorous, self-contained, with many examples and exercises." (Zentralblatt MATH Vol. 968, 2001/18) "This well-organized book...is certainly useful...It is a valuable source for a deeper and more precise understanding of the behaviors of algorithms on sequences." (Mathematical Reviews, 2002f)Table of ContentsForeword. Preface. Acknowledgments. PROBLEMS ON WORDS. Data Structures and Algorithms on Words. Probabilistic and Analytical Models. PROBABILISTIC AND COMBINATORIAL TECHNIQUES. Inclusion-Exclusion Principle. The First and Second Moment Methods. Subadditive Ergodic Theorem and Large Deviations. Elements of Information Theory. ANALYTIC TECHNIQUES. Generating Functions. Complex Asymptotic Methods. Mellin Transform and Its Applications. Analytic Poissonization and Depoissonization. Bibliography. Index.
£165.56
John Wiley & Sons Inc Linear Systems Fourier Transforms and Optics
Book SynopsisA complete and balanced account of communication theory, providing an understanding of both Fourier analysis (and the concepts associated with linear systems) and the characterization of such systems by mathematical operators. Presents applications of the theories to the diffraction of optical wave-fields and the analysis of image-forming systems.Table of ContentsRepresentation of Physical Quantities by MathematicalFunctions. Special Functions. Harmonic Analysis. Mathematical Operators and Physical Systems. Convolution. The Fourier Transform. Characteristics and Applications of Linear Filters. Two-Dimensional Convolution and Fourier Transformation. The Propagation and Diffraction of Optical Wave Fields. Image-Forming Systems. Appendices. Index.
£211.46
John Wiley & Sons Inc Applied Multivariate Techniques
Book SynopsisThis book focuses on when to use the various analytic techniques and how to interpret the resulting output from the most widely used statistical packages (e.g. , SAS, SPSS).Table of ContentsGeometric Concepts of Data Manipulation. Fundamentals of Data Manipulation. Principal Components Analysis. Factor Analysis. Confirmatory Factor Analysis. Cluster Analysis. Two-Group Discriminant Analysis. Multiple-Group Discriminant Analysis. Logistic Regression. Multivariate Analysis of Variance. Assumptions. Canonical Correlation. Covariance Structure Models. Statistical Tables. References. Tables, Figures, and Exhibits. Index.
£223.16
John Wiley & Sons Inc Trading on the Edge
Book SynopsisExperts from the world''s major financial institutions contributed to this work and have already used the newest technologies. Gives proven strategies for using neural networks, algorithms, fuzzy logic and nonlinear data analysis techniques to enhance profitability. The latest analytical breakthroughs, the impact on modern finance theory and practice, including the best ways for profitably applying them to any trading and portfolio management system, are all covered.Table of ContentsPartial table of contents: TRADING WITH NEURAL NETWORKS. Neural Network Techniques (C. Klimasauskas). Adaptive Selection of U.S. Stocks with Neural Nets (J. Hall). Intelligent Trading of an Emerging Market (G.-S. Jang & F. Lai). Neural Nets for Foreign Exchange Trading (H. Green & M.Pearson). STRATEGY OPTIMIZATION WITH GENETIC ALGORITHMS. Genetic Algorithms for Financial Modeling (A. Colin). PORTFOLIO MANAGEMENT USING FUZZY LOGIC. Why Use Fuzzy Modeling? (G. Deboeck). Smart Trading with FRET (D. Benachenhou). NONLINEAR DYNAMICS AND CHAOS. Nonlinear Data Analysis Techniques (T. Frison). RISK MANAGEMENT AND THE IMPACT OF TECHNOLOGY. The Cutting Edge of Trading Technology (G. Deboeck). Glossary. Bibliography. Index.
£60.00
John Wiley & Sons Inc Sampling Methods for Multiresource Forest
Book SynopsisDesigned to aid readers in gathering the most reliable quantitative information on forests for the least cost.Table of ContentsFocus, Fundamental Concepts, and Theory. Probabilistic Sampling Strategies. Forest Sampling--Single Level. Multi-Information Sources for Sampling. Model-Based Inference. Mensurational Aspects of Forest Inventory. Related Sampling Topics. Related Estimation Topics. Future Directions in Multiresource Sampling in Forestry. References. Answers to the Problems. Index.
£248.36
Wiley Continuous Univariate Distributions Volume 2
Book SynopsisThis volume presents a detailed description of the statistical distributions that are commonly applied to such fields as engineering, business, economics and the behavioural, biological and environmental sciences.Table of ContentsExtreme Value Distributions. Logistic Distribution. Laplace (Double Exponential) Distributions. Beta Distributions. Uniform (Rectangular) Distributions. F-Distributions. t-Distributions. Noncentral x^2 Distributions. Noncentral F-Distributions. Noncentral t-Distributions. Distributions of Correlation Coefficients. Lifetime Distributions and Miscellaneous Orderings. Abbreviations. Indexes.
£206.96
John Wiley & Sons Inc Continuous Univariate Distributions Volume 1
Book SynopsisThe definitive reference for statistical distributions Continuous Univariate Distributions, Volume 1 offers comprehensive guidance toward the most commonly used statistical distributions, including normal, lognormal, inverse Gaussian, Pareto, Cauchy, gamma distributions and more. Each distribution includes clear definitions and properties, plus methods of inference, applications, algorithms, characterizations, and reference to other related distributions. Organized for easy navigation and quick reference, this book is an invaluable resource for investors, data analysts, or anyone working with statistical distributions on a regular basis.Table of ContentsContinuous Distributions (General). Normal Distributions. Lognormal Distributions. Inverse Gaussian (Wald) Distributions. Cauchy Distribution. Gamma Distributions. Chi-Square Distributions Including Chi and Rayleigh. Exponential Distributions. Pareto Distributions. Weibull Distributions. Abbreviations. Indexes.
£206.96
John Wiley & Sons Inc Applied and Computational Complex Analysis Volume
Book SynopsisPresents applications as well as the basic theory of analytic functions of one or several complex variables. The first volume discusses applications and basic theory of conformal mapping and the solution of algebraic and transcendental equations. Volume Two covers topics broadly connected with ordinary differental equations: special functions, integral transforms, asymptotics and continued fractions. Volume Three details discrete fourier analysis, cauchy integrals, construction of conformal maps, univalent functions, potential theory in the plane and polynomial expansions.Table of ContentsFormal Power Series. Functions Analytic at a Point. Analytic Continuation. Complex Integration. Conformal Mapping. Polynomials. Partial Fractions. Bibliography. Index.
£165.56
John Wiley & Sons Inc Fourier Analysis on Finite Groups with
Book SynopsisThis book examines applications of Fourier analysis on finite non-Abelian groups, and discusses different methods to determine compact representations for discrete functions providing for their efficient realizations and related applications. Switching functions are included as a particular example of discrete functions in engineering practice.Trade Review"…a concise monograph about the algebraic structures theory used in the Fourier analysis of signals and systems…useful for applied mathematicians and for engineers…" (Computing Reviews.com, November 3, 2005)Table of ContentsPreface. Acknowledgments. Acronyms. 1 Signals and Their Mathematical Models. 1.1 Systems. 1.2 Signals. 1.3 Mathematical Models of Signals. References. 2 Fourier Analysis. 2.1 Representations of Groups. 2.1.1 Complete Reducibility. 2.2 Fourier Transform on Finite Groups. 2.3 Properties of the Fourier Transform. 2.4 Matrix Interpretation of the Fourier Transform on Finite Non-Abelian Groups. 2.5 Fast Fourier Transform on Finite Non-Abelian Groups. References. 3 Matrix Interpretation of the FFT. 3.1 Matrix Interpretation of FFT on Finite Non-Abelian Groups. 3.2 Illustrative Examples. 3.3 Complexity of the FFT. 3.3.1 Complexity of Calculations of the FFT. 3.3.2 Remarks on Programming Implememtation of FFT. 3.4 FFT Through Decision Diagrams. 3.4.1 Decision Diagrams. 3.4.2 FFT on Finite Non-Abelian Groups Through DDs. 3.4.3 MMTDs for the Fourier Spectrum. 3.4.4 Complexity of DDs Calculation Methods. References. 4 Optimization of Decision Diagrams. 4.1 Reduction Possibilities in Decision Diagrams. 4.2 Group-Theoretic Interpretation of DD. 4.3 Fourier Decision Diagrams. 4.3.1 Fourier Decision Trees. 4.3.2 Fourier Decision Diagrams. 4.4 Discussion of Different Decompositions. 4.4.1 Algorithm for Optimization of DDs. 4.5 Representation of Two-Variable Function Generator. 4.6 Representation of Adders by Fourier DD. 4.7 Representation of Multipliers by Fourier DD. 4.8 Complexity of NADD. 4.9 Fourier DDs with Preprocessing. 4.9.1 Matrix-valued Functions. 4.9.2 Fourier Transform for Matrix-Valued Functions. 4.10 Fourier Decision Trees with Preprocessing. 4.11 Fourier Decision Diagrams with Preprocessing. 4.12 Construction of FNAPDD. 4.13 Algorithm for Construction of FNAPDD. 4.13.1 Algorithm for Representation. 4.14 Optimization of FNAPDD. References. 5 Functional Expressions on Quaternion Groups. 5.1 Fourier Expressions on Finite Dyadic Groups. 5.1.1 Finite Dyadic Groups. 5.2 Fourier Expressions on Q2. 5.3 Arithmetic Expressions. 5.4 Arithmetic Expressions from Walsh Expansions. 5.5 Arithmetic Expressions on Q2. 5.5.1 Arithmetic Expressions and Arithmetic-Haar Expressions. 5.5.2 Arithmetic-Haar Expressions and Kronecker Expressions. 5.6 Different Polarity Polynomials Expressions. 5.6.1 Fixed-Polarity Fourier Expressions in C(Q2). 5.6.2 Fixed-Polarity Arithmetic-Haar Expressions. 5.7 Calculation of the Arithmetic-Haar Coefficients. 5.7.1 FFT-like Algorithm. 5.7.2 Calculation of Arithmetic-Haar Coefficients Through Decision Diagrams. References. 6 Gibbs Derivatives on Finite Groups. 6.1 Definition and Properties of Gibbs Derivatives on Finite Non-Abelian Groups. 6.2 Gibbs Anti-Derivative. 6.3 Partial Gibbs Derivatives. 6.4 Gibbs Differential Equations. 6.5 Matrix Interpretation of Gibbs Derivatives. 6.6 Fast Algorithms for Calculation of Gibbs Derivatives on Finite Groups. 6.6.1 Complexity of Calculation of Gibbs Derivatives. 6.7 Calculation of Gibbs Derivatives Through DDs. 6.7.1 Calculation of Partial Gibbs Derivatives. References. 7 Linear Systems on Finite Non-Abelian Groups. 7.1 Linear Shift-Invariant Systems on Groups. 7.2 Linear Shift-Invariant Systems on Finite Non-Abelian Groups. 7.3 Gibbs Derivatives and Linear Systems. 7.3.1 Discussion. References. 8 Hilbert Transform on Finite Groups. 8.1 Some Results of Fourier Analysis on Finite Non-Abelian Groups. 8.2 Hilbert Transform on Finite Non-Abelian Groups. 8.3 Hilbert Transform in Finite Fields. References. Index.
£100.76
John Wiley & Sons Inc Culinary Calculations
Book SynopsisCulinary Calculations, Second Edition provides the mathematical knowledge and skills that are essential for a successful career in today''s competitive food service industry. This user-friendly guide starts with basic principles before introducing more specialized topics like costing, AP/EP, menu pricing, recipe conversion and costing, and inventory costs. Written in a non-technical, easy-to-understand style, the book features a case study that runs through all chapters, showing the various math concepts put into real-world practice. This revised and updated Second Edition of Culinary Calculations covers relevant math skills for four key areas: Basic math for the culinary arts and food service industry Math for the professional kitchen Math for the business side of the food service industry Computer applications for the food service industry Each chapter within these sections is rich with resources, inTable of ContentsIntroduction: Importance of Mathematics to the Food Service Industry. A. Math in the Kitchen. 1. Units of Measure. 2. Food Yield. 3. Recipes. 4. Portion. B. Math for Business Operations. 1. Profit and Non Profit Food Service. 2. Basic accounting terminology. 3. Purchasing. 4. Inventory management. 5. Menu Pricing. C. Case Study Introduction. SECTION I: BASIC MATHEMATICS FOR THE CULINARY ARTS AND FOOD SERVICE INDUSTRY. Chapter 1. Basic Mathematics with Whole Numbers. A. Addition. B. Subtraction. C. Multiplication. D. Division. Chapter 2: Applied Math Problems with Simple Solutions. Chapter 3: Mixed Numbers and Non-integer Quantities. A. Fractions. B. Decimals. C. Percents. Chapter 4: Basic Mathematical Operations with Mixed Numbers and Non-integer Quantities. A. Fractions. B. Mixed Numbers. C. Decimals. D. Percents. Chapter 5: Basic Mathematics: Additional Information and Tips for Success. A.Rounding and Estimation. B.Multipliers and Conversion Factors. C.Ratios. D.Proportion. E.Greater Than, Less Than. SECTION II: MATHEMATICS FOR THE PROFESSIONAL KITCHEN. Chapter 6: Standardized Recipes. A. Format. B. Importance of the information contained. C. Continual Case Study Steps I and II. Chapter 7: Units of Measure. A. United States Standard Units of Measure. B. Metric Units of Measure. C. Comparison of US Standard Units to Metric Units. D. Conversion of US Standard to Metric. E. Conversion of Metric to US Standard. F. Conversion of Volume to Weight. G. Conversion of Weight to Volume. H. Continual Case Study Step III. Chapter 8: Food Service Specific Terminology and Mathematics. Part I: As Purchased, Edible Portion, As Served, Yield Percent. A. As Purchased . B. Edible Portion . C. As Served Portion. D. Yield Percent. E. Average Yield Percent Chart. Chapter 9: Food Service Specific Terminology and Mathematics. Part II: The Impact of As Purchased and Edible Portion on the Major Food Groups. A. Food Purchasing. B. Food Product Groups. C. Meats. D. Yield Test. E. Produce. F. Dairy Products. G. Pasta, Rice, and Legumes. H. Miscellaneous Items. I. Edible Portion and As Served . Chapter 10: Food Service Specific Terminology and Mathematics. Part III: Recipe and Portion Costing. A. Relationship between As Purchased and Edible Portion. B. Approximate or Average Yield Percent. C. Recipe Costing using the Approximate or Average Yield Percent. D. Steps to calculate a recipe cost: Simple and Common examples. E. Miscellaneous Ingredient Cost. F. Additional costs to serve a guest a meal. G. Continual Case Study: Step IV. SECTION III: MATH FOR THE BUSINESS SIDE OF THE FOOD SERVICE INDUSTRY. Chapter 11: Menu Pricing. A. A la carte, Table d’hote, and Prix Fixe pricing. B. Food Cost and Food Cost Percent pricing. C. Limitations of Food Cost pricing. D. Additional Menu Pricing Techniques. E. Alcoholic Beverages, Alcoholic Beverage Cost and Percent . F. Alcoholic Beverage Menu Pricing. G. Bakery and Pastry Industry Pricing. E. Case Study Step V. Chapter 12: Basic Accounting for Food Service Operations also Known as The Impact of Menu Pricing on Success and Profit. A. Revenue. B. Cost. 1.The cost of energy. C. Profit and Loss. D. Case Study Step VI. Chapter 13: Labor Cost and Control Techniques. A. Labor Costs. B. Staffing Guide. C. Employee Schedules. D. Labor Cost Control. E. Case Study Step VII. Chapter 14: Purchasing and Inventory Management. A. Purchasing Food Products. B. Inventory Management. C. Inventory Quantities. D. Cost of Goods Sold. E. Inventory Turnover. E. Case Study Step VIII. SECTION IV: COMPUTER APPLICATIONS FOR THE FOOD SERVICE INDUSTRY. Chapter 15: Computer Applications for the Food Service Industry. A. Point of Sale Technology. B. Inventory Purchasing Software. C. Menu Printing. D. Case Study Step IX.
£56.00
John Wiley & Sons Inc Effective Groundwater Model Calibration With
Book SynopsisMethods and guidelines for developing and using mathematical models Turn to Effective Groundwater Model Calibration for a set of methods and guidelines that can help produce more accurate and transparent mathematical models. The models can represent groundwater flow and transport and other natural and engineered systems.Trade Review"This is an excellent textbook that addresses a topic, optimization of multiparameter models, which is of broad interest." (Journal of American Water Resources Association, October 2007) "The book represents a very good combination of long-time expert knowledge and being up to date." (Clean, January 2008) "…a welcome addition to my collection of hydrogeologic books…a valuable reference for ground water scientists who use models." (Ground Water, January-February 2008)Table of ContentsPreface. 1 Introduction. 1.1 Book and Associated Contributions: Methods, Guidelines, Exercises, Answers, Software, and PowerPoint Files. 1.2 Model Calibration with Inverse Modeling. 1.2.1 Parameterization. 1.2.2 Objective Function. 1.2.3 Utility of Inverse Modeling and Associated Methods. 1.2.4 Using the Model to Quantitatively Connect Parameters, Observations, and Predictions. 1.3 Relation of this Book to Other Ideas and Previous Works. 1.3.1 Predictive Versus Calibrated Models. 1.3.2 Previous Work. 1.4 A Few Definitions. 1.4.1 Linear and Nonlinear. 1.4.2 Precision, Accuracy, Reliability, and Uncertainty. 1.5 Advantageous Expertise and Suggested Readings. 1.6 Overview of Chapters 2 Through 15. 2 Computer Software and Groundwater Management Problem Used in the Exercises. 2.1 Computer Programs MODFLOW-2000, UCODE_2005, and PEST. 2.2 Groundwater Management Problem Used for the Exercises. 2.2.1 Purpose and Strategy. 2.2.2 Flow System Characteristics. 2.3 Exercises. Exercise 2.1: Simulate Steady-State Heads and Perform Preparatory Steps. 3 Comparing Observed and Simulated Values Using Objective Functions. 3.1 Weighted Least-Squares Objective Function. 3.1.1 With a Diagonal Weight Matrix. 3.1.2 With a Full Weight Matrix. 3.2 Alternative Objective Functions. 3.2.1 Maximum-Likelihood Objective Function. 3.2.2 L1 Norm Objective Function. 3.2.3 Multiobjective Function. 3.3 Requirements for Accurate Simulated Results. 3.3.1 Accurate Model. 3.3.2 Unbiased Observations and Prior Information. 3.3.3 Weighting Reflects Errors. 3.4 Additional Issues. 3.4.1 Prior Information. 3.4.2 Weighting. 3.4.3 Residuals and Weighted Residuals. 3.5 Least-Squares Objective-Function Surfaces. 3.6 Exercises. Exercise 3.1: Steady-State Parameter Definition. Exercise 3.2: Observations for the Steady-State Problem. Exercise 3.3: Evaluate Model Fit Using Starting Parameter Values. 4 Determining the Information that Observations Provide on Parameter Values using Fit-Independent Statistics. 4.1 Using Observations. 4.1.1 Model Construction and Parameter Definition. 4.1.2 Parameter Values. 4.2 When to Determine the Information that Observations Provide About Parameter Values. 4.3 Fit-Independent Statistics for Sensitivity Analysis. 4.3.1 Sensitivities. 4.3.2 Scaling. 4.3.3 Dimensionless Scaled Sensitivities (dss). 4.3.4 Composite Scaled Sensitivities (css). 4.3.5 Parameter Correlation Coefficients (pcc). 4.3.6 Leverage Statistics. 4.3.7 One-Percent Scaled Sensitivities. 4.4 Advantages and Limitations of Fit-Independent Statistics for Sensitivity Analysis. 4.4.1 Scaled Sensitivities. 4.4.2 Parameter Correlation Coefficients. 4.4.3 Leverage Statistics. 4.5 Exercises. Exercise 4.1: Sensitivity Analysis for the Steady-State Model with Starting Parameter Values. 5 Estimating Parameter Values. 5.1 The Modified Gauss–Newton Gradient Method. 5.1.1 Normal Equations. 5.1.2 An Example. 5.1.3 Convergence Criteria. 5.2 Alternative Optimization Methods. 5.3 Multiobjective Optimization. 5.4 Log-Transformed Parameters. 5.5 Use of Limits on Estimated Parameter Values. 5.6 Exercises. Exercise 5.1: Modified Gauss–Newton Method and Application to a Two-Parameter Problem. Exercise 5.2: Estimate the Parameters of the Steady-State Model. 6 Evaluating Model Fit. 6.1 Magnitude of Residuals and Weighted Residuals. 6.2 Identify Systematic Misfit. 6.3 Measures of Overall Model Fit. 6.3.1 Objective-Function Value. 6.3.2 Calculated Error Variance and Standard Error. 6.3.3 AIC, AICc, and BIC Statistics. 6.4 Analyzing Model Fit Graphically and Related Statistics. 6.4.1 Using Graphical Analysis of Weighted Residuals to Detect Model Error. 6.4.2 Weighted Residuals Versus Weighted or Unweighted Simulated Values and Minimum, Maximum, and Average Weighted Residuals. 6.4.3 Weighted or Unweighted Observations Versus Simulated Values and Correlation Coefficient R. 6.4.4 Graphs and Maps Using Independent Variables and the Runs Statistic. 6.4.5 Normal Probability Graphs and Correlation Coefficient RN2. 6.4.6 Acceptable Deviations from Random, Normally Distributed Weighted Residuals. 6.5 Exercises. Exercise 6.1: Statistical Measures of Overall Fit. Exercise 6.2: Evaluate Graph Model fit and Related Statistics. 7 Evaluating Estimated Parameter Values and Parameter Uncertainty. 7.1 Reevaluating Composite Scaled Sensitivities. 7.2 Using Statistics from the Parameter Variance–Covariance Matrix. 7.2.1 Five Versions of the Variance–Covariance Matrix. 7.2.2 Parameter Variances, Covariances, Standard Deviations, Coefficients of Variation, and Correlation Coefficients. 7.2.3 Relation Between Sample and Regression Statistics. 7.2.4 Statistics for Log-Transformed Parameters. 7.2.5 When to Use the Five Versions of the Parameter Variance–Covariance Matrix. 7.2.6 Some Alternate Methods: Eigenvectors, Eigenvalues, and Singular Value Decomposition. 7.3 Identifying Observations Important to Estimated Parameter Values. 7.3.1 Leverage Statistics. 7.3.2 Influence Statistics. 7.4 Uniqueness and Optimality of the Estimated Parameter Values. 7.5 Quantifying Parameter Value Uncertainty. 7.5.1 Inferential Statistics. 7.5.2 Monte Carlo Methods. 7.6 Checking Parameter Estimates Against Reasonable Values. 7.7 Testing Linearity. 7.8 Exercises. Exercise 7.1: Parameter Statistics. Exercise 7.2: Consider All the Different Correlation Coefficients Presented. Exercise 7.3: Test for Linearity. 8 Evaluating Model Predictions, Data Needs, and Prediction Uncertainty. 8.1 Simulating Predictions and Prediction Sensitivities and Standard Deviations. 8.2 Using Predictions to Guide Collection of Data that Directly Characterize System Properties. 8.2.1 Prediction Scaled Sensitivities (pss). 8.2.2 Prediction Scaled Sensitivities Used in Conjunction with Composite Scaled Sensitivities. 8.2.3 Parameter Correlation Coefficients without and with Predictions. 8.2.4 Composite and Prediction Scaled Sensitivities Used with Parameter Correlation Coefficients. 8.2.5 Parameter–Prediction ( ppr) Statistic. 8.3 Using Predictions to Guide Collection of Observation Data. 8.3.1 Use of Prediction, Composite, and Dimensionless Scaled Sensitivities and Parameter Correlation Coefficients. 8.3.2 Observation–Prediction (opr) Statistic. 8.3.3 Insights About the opr Statistic from Other Fit-Independent Statistics. 8.3.4 Implications for Monitoring Network Design. 8.4 Quantifying Prediction Uncertainty Using Inferential Statistics. 8.4.1 Definitions. 8.4.2 Linear Confidence and Prediction Intervals on Predictions. 8.4.3 Nonlinear Confidence and Prediction Intervals. 8.4.4 Using the Theis Example to Understand Linear and Nonlinear Confidence Intervals. 8.4.5 Differences and Their Standard Deviations, Confidence Intervals, and Prediction Intervals. 8.4.6 Using Confidence Intervals to Serve the Purposes of Traditional Sensitivity Analysis. 8.5 Quantifying Prediction Uncertainty Using Monte Carlo Analysis. 8.5.1 Elements of a Monte Carlo Analysis. 8.5.2 Relation Between Monte Carlo Analysis and Linear and Nonlinear Confidence Intervals. 8.5.3 Using the Theis Example to Understand Monte Carlo Methods. 8.6 Quantifying Prediction Uncertainty Using Alternative Models. 8.7 Testing Model Nonlinearity with Respect to the Predictions. 8.8 Exercises. Exercise 8.1: Predict Advective Transport and Perform Sensitivity Analysis. Exercise 8.2: Prediction Uncertainty Measured Using Inferential Statistics. 9 Calibrating Transient and Transport Models and Recalibrating Existing Models. 9.1 Strategies for Calibrating Transient Models. 9.1.1 Initial Conditions. 9.1.2 Transient Observations. 9.1.3 Additional Model Inputs. 9.2 Strategies for Calibrating Transport Models. 9.2.1 Selecting Processes to Include. 9.2.2 Defining Source Geometry and Concentrations. 9.2.3 Scale Issues. 9.2.4 Numerical Issues: Model Accuracy and Execution Time. 9.2.5 Transport Observations. 9.2.6 Additional Model Inputs. 9.2.7 Examples of Obtaining a Tractable, Useful Model. 9.3 Strategies for Recalibrating Existing Models. 9.4 Exercises (optional). Exercises 9.1 and 9.2: Simulate Transient Hydraulic Heads and Perform Preparatory Steps. Exercise 9.3: Transient Parameter Definition. Exercise 9.4: Observations for the Transient Problem. Exercise 9.5: Evaluate Transient Model Fit Using Starting Parameter Values. Exercise 9.6: Sensitivity Analysis for the Initial Model. Exercise 9.7: Estimate Parameters for the Transient System by Nonlinear Regression. Exercise 9.8: Evaluate Measures of Model Fit. Exercise 9.9: Perform Graphical Analyses of Model Fit and Evaluate Related Statistics. Exercise 9.10: Evaluate Estimated Parameters. Exercise 9.11: Test for Linearity. Exercise 9.12: Predictions. 10 Guidelines for Effective Modeling. 10.1 Purpose of the Guidelines. 10.2 Relation to Previous Work. 10.3 Suggestions for Effective Implementation. 11 Guidelines 1 Through 8—Model Development. Guideline 1: Apply the Principle of Parsimony. G1.1 Problem. G1.2 Constructive Approaches. Guideline 2: Use a Broad Range of System Information to Constrain the Problem. G2.1 Data Assimilation. G2.2 Using System Information. G2.3 Data Management. G2.4 Application: Characterizing a Fractured Dolomite Aquifer. Guideline 3: Maintain a Well-Posed, Comprehensive Regression Problem. G3.1 Examples. G3.2 Effects of Nonlinearity on the css and pcc. Guideline 4: Include Many Kinds of Data as Observations in the Regression. G4.1 Interpolated “Observations”. G4.2 Clustered Observations. G4.3 Observations that Are Inconsistent with Model Construction. G4.4 Applications: Using Different Types of Observations to Calibrate Groundwater Flow and Transport Models. Guideline 5: Use Prior Information Carefully. G5.1 Use of Prior Information Compared with Observations. G5.2 Highly Parameterized Models. G5.3 Applications: Geophysical Data. Guideline 6: Assign Weights that Reflect Errors. G6.1 Determine Weights. G6.2 Issues of Weighting in Nonlinear Regression. Guideline 7: Encourage Convergence by Making the Model More Accurate and Evaluating the Observations. Guideline 8: Consider Alternative Models. G8.1 Develop Alternative Models. G8.2 Discriminate Between Models. G8.3 Simulate Predictions with Alternative Models. G8.4 Application. 12 Guidelines 9 and 10—Model Testing. Guideline 9: Evaluate Model Fit. G9.1 Determine Model Fit. G9.2 Examine Fit for Existing Observations Important to the Purpose of the Model. G9.3 Diagnose the Cause of Poor Model Fit. Guideline 10: Evaluate Optimized Parameter Values. G10.1 Quantify Parameter-Value Uncertainty. G10.2 Use Parameter Estimates to Detect Model Error. G10.3 Diagnose the Cause of Unreasonable Optimal Parameter Estimates. G10.4 Identify Observations Important to the Parameter Estimates. G10.5 Reduce or Increase the Number of Parameters. 13 Guidelines 11 and 12—Potential New Data. Guideline 11: Identify New Data to Improve Simulated Processes, Features, and Properties. Guideline 12: Identify New Data to Improve Predictions. G12.1 Potential New Data to Improve Features and Properties Governing System Dynamics. G12.2 Potential New Data to Support Observations. 14 Guidelines 13 and 14—Prediction Uncertainty. Guideline 13: Evaluate Prediction Uncertainty and Accuracy Using Deterministic Methods. G13.1 Use Regression to Determine Whether Predicted Values Are Contradicted by the Calibrated Model. G13.2 Use Omitted Data and Postaudits. Guideline 14: Quantify Prediction Uncertainty Using Statistical Methods. G14.1 Inferential Statistics. G14.2 Monte Carlo Methods. 15 Using and Testing the Methods and Guidelines. 15.1 Execution Time Issues. 15.2 Field Applications and Synthetic Test Cases. 15.2.1 The Death Valley Regional Flow System, California and Nevada, USA. 15.2.2 Grindsted Landfill, Denmark. Appendix A: Objective Function Issues. A.1 Derivation of the Maximum-Likelihood Objective Function. A.2 Relation of the Maximum-Likelihood and Least-Squares Objective Functions. A.3 Assumptions Required for Diagonal Weighting to be Correct. A.4 References. Appendix B: Calculation Details of the Modified Gauss–Newton Method. B.1 Vectors and Matrices for Nonlinear Regression. B.2 Quasi-Newton Updating of the Normal Equations. B.3 Calculating the Damping Parameter. B.4 Solving the Normal Equations. B.5 References. Appendix C: Two Important Properties of Linear Regression and the Effects of Nonlinearity. C.1 Identities Needed for the Proofs. C.1.1 True Linear Model. C.1.2 True Nonlinear Model. C.1.3 Linearized True Nonlinear Model. C.1.4 Approximate Linear Model. C.1.5 Approximate Nonlinear Model. C.1.6 Linearized Approximate Nonlinear Model. C.1.7 The Importance of X and X. C.1.8 Considering Many Observations. C.1.9 Normal Equations. C.1.10 Random Variables. C.1.11 Expected Value. C.1.12 Variance–Covariance Matrix of a Vector. C.2 Proof of Property 1: Parameters Estimated by Linear Regression are Unbiased. C.3 Proof of Property 2: The Weight Matrix Needs to be Defined in a Particular Way for Eq. (7.1) to Apply and for the Parameter Estimates to have the Smallest Variance. C.4 References. Appendix D: Selected Statistical Tables. D.1 References. References. Index.
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John Wiley & Sons Inc Statistical Methods for Geographers
Book SynopsisA textbook for advanced undergraduate/first year graduate level courses in statistical methods in geography. Presents methods useful in research design, hypothesis testing, and analyzing spatial and functional relationships.Table of ContentsAn Introduction to Statistical Methods. The Display of Distributions. Statistical Summaries of Distributions. Probability and Probability Functions. Sampling Designs and Sampling Methods. Statistical Inference: Fitting Probability Functions. Statistical Inference: Interval Estimation and HypothesisTesting. An Introduction to Bivariate Relationships. The Simple Linear Regression Model. The General Linear Model--Multiple Regression. Issues in the Application of the General Linear Model. Extensions of Multivariate Linear Regression Methods. Alternative Forms of Multivariate Analysis. Index.
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Book SynopsisThe arguments in this fascinating, interdisciplinary book are wide-ranging, running the gamut from company management to the nature of consciousness. The author discusses the theory of team syntegrity and the social technique of syntegration which works in practice, offering a potent management tool for developmental planning.Table of ContentsTHE STORY OF AN ORGANIZATIONAL IDEA. A Long Gestation. On Protocols. Path-Finding Experiments. The Academic Milieu. The Corporate Scene. In the Community. ENHANCING PROCEDURES. Protocols Revisited. Vexed Questions of Allocation. Developmental Planning. Governance or Government?. THE FORM OF THE MODEL. The Structure of Icosahedral Space. The Dynamics of Icosahedral Space. Self-Reference in Icosahedral Space. EPILOGUE. The Concept of Recursive Consciousness. COLLABORATORS' SURPLUS. Reverberating Networks: Modelling Information Propagation inSyntegration by Spectral Analysis (A. Jalali). From Prototype to Protocol: Design for Doing (J. Truss). Pliny the Later: Elective Selection (J. Hancock). You Drive for Show but You Putt for Dough: A Facilitator'sPerspective (A. Pearson). One Man's Signal Is Another Man's Noise: Another Facilitator'sPerspective (D. Beatty). About Face: A Turn for Better Planning (J. Truss). The Very Model of a Modern System-General: How the Viable SystemModel Actually Works (A. Leonard). References. Index.
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£215.06
