Maths for scientists Books

Book SynopsisThis text provides a critique of the subjective Bayesian view of statistical inference, and proposes the author's own error-statistical approach as an alternative framework for the epistemology of experiment. It seeks to address the needs of researchers who work with statistical analysis.

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Book SynopsisBased on a course taught by the author, this book combines the theoretical underpinnings of statistics with the practical analysis of Earth sciences data using MATLAB. The book is organized to introduce the underlying concepts, and then extends these to the data, covering methods that are most applicable to Earth sciences. Topics include classical parametric estimation and hypothesis testing, and more advanced least squares-based, nonparametric, and resampling estimators. Multivariate data analysis, not often encountered in introductory texts, is presented later in the book, and compositional data is treated at the end. Datasets and bespoke MATLAB scripts used in the book are available online, as well as additional datasets and suggested questions for use by instructors. Aimed at entering graduate students and practicing researchers in the Earth and ocean sciences, this book is ideal for those who want to learn how to analyse data using MATLAB in a statistically-rigorous manner.Trade Review'One of the main strengths of this book is the combination of mathematical rigor with extensive examples, allowing readers to work through case studies to better understand the concepts presented. The tool used for this purpose is MATLAB, which is widely used in the earth science community. Examples are drawn from geophysics, astrophysics, and anthropology (among others). Both the scripts and the data examples used in the book are available for download from the publisher's website. … This book is an ideal guide for graduate students seeking a comprehensive and rigorous understanding of statistical methods in earth sciences. For the more mature earth scientist (and I include myself in that number), it provides a useful reference to widely used statistical concepts that many of us regularly encounter.' Lucy MacGregor, The Leading Edge'… this book will be a welcome and invaluable addition to any earth scientist's library.' Sven Treitel, The Leading EdgeTable of ContentsPreface; 1. Probability concepts; 2. Statistical concepts; 3. Statistical distributions; 4. Characterization of data; 5. Point, interval and ratio estimators; 6. Hypothesis testing; 7. Nonparametric methods; 8. Resampling methods; 9. Linear regression; 10. Multivariate statistics; 11. Compositional data; Appendix: MATLAB functions to produce ternary diagrams; References; Index.

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Book SynopsisThis is an introductory textbook on spatial analysis and spatial statistics through GIS. Each chapter presents methods and metrics, explains how to interpret results, and provides worked examples. Topics include: describing and mapping data through exploratory spatial data analysis; analyzing geographic distributions and point patterns; spatial autocorrelation; spatial clustering; geographically weighted regression and OLS regression; and spatial econometrics. The worked examples link theory to practice through a single real-world case study, with software and illustrated guidance. Exercises are solved twice: first through ArcGIS, and then GeoDa. Through a simple methodological framework the book describes the dataset, explores spatial relations and associations, and builds models. Results are critically interpreted, and the advantages and pitfalls of using various spatial analysis methods are discussed. This is a valuable resource for graduate students and researchers analyzing geospatial data through a spatial analysis lens, including those using GIS in the environmental sciences, geography, and social sciences.Trade Review'… the perfect introduction to the emerging field of spatial data science. It is clearly written, with realistic and carefully worked out examples and based on a sound pedagogical approach.' Luc Anselin, Director, Center for Spatial Data Science, University of Chicago, and creator of the GeoDa software'… An excellent course text for students of GIS, spatial statistics, quantitative geography, and ecology … Essential reading for beginning students as well as those who wish to refresh their knowledge with respect to newer tools such as geographically weighted regression and spatial econometrics … introduces spatial analysis to those with very little training in statistics while at the same time developing applications using standard software for spatial analysis based on the ArcGIS and Geoda software systems. An excellent primer for anyone following a full course in spatial analysis. Spatial analysis is a tough subject to teach but Grekousis guides the reader through the basic ideas about understanding how correlations define our geographic world, introducing the full range of spatial tools and models.' Michael Batty, Centre for Advanced Spatial Analysis (CASA), University College London'Highly valuable and timely book for multidisciplinary professionals and students who aim to work with spatial problems but do not yet have the tools to study and solve these. The book provides an excellent introduction to the concepts and tools to think and analyse spatially, complemented by practical, realistic examples of how to apply this knowledge. The book has sufficient depth and rigor to allow students at all levels to learn for themselves and reach a good comprehension of a wide variety of aspects within this scientific domain.' Walter T. de Vries, Technical University of Munich'… an excellent blend of key theoretical concepts and applications. It covers a wide range of spatial topics and concepts while progressively building up in difficulty. The engaging examples, demonstrative code and laboratory follow-up exercises make this book suitable for both self-learners and traditional academic settings. Highly recommended.' Giorgos Mountrakis, State University of New York'A much welcomed and timely addition to the bookshelf of practitioners interested in the quantitative analysis of geographical data. The book offers a clear and concise exposition to basic and advanced methods and tools of spatial analysis, solidifying understanding through worked real-world case studies based on state-of-the-art commercial (ArcGIS) and public-domain (GeoDA) software. Definitely a book to be routinely used as a reference on the practical implementation of key analytical methods by people employing geographical data across a wide spectrum of disciplines.' Phaedon Kyriakidis, Cyprus University of TechnologyTable of Contents1. Think spatially: basic concepts of spatial analysis and space conceptualization; 2. Exploratory spatial data analysis tools and statistics; 3. Analyzing geographic distributions and point patterns; 4. Spatial autocorrelation; 5. Multivariate data in geography: data reduction and clustering; 6. Modeling relationships: regression and geographically weighted regression; 7. Spatial econometrics.

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Book SynopsisThis is an introductory textbook on spatial analysis and spatial statistics through GIS. Each chapter presents methods and metrics, explains how to interpret results, and provides worked examples. Topics include: describing and mapping data through exploratory spatial data analysis; analyzing geographic distributions and point patterns; spatial autocorrelation; spatial clustering; geographically weighted regression and OLS regression; and spatial econometrics. The worked examples link theory to practice through a single real-world case study, with software and illustrated guidance. Exercises are solved twice: first through ArcGIS, and then GeoDa. Through a simple methodological framework the book describes the dataset, explores spatial relations and associations, and builds models. Results are critically interpreted, and the advantages and pitfalls of using various spatial analysis methods are discussed. This is a valuable resource for graduate students and researchers analyzing geospaTrade Review'… the perfect introduction to the emerging field of spatial data science. It is clearly written, with realistic and carefully worked out examples and based on a sound pedagogical approach.' Luc Anselin, Director, Center for Spatial Data Science, University of Chicago, and creator of the GeoDa software'… An excellent course text for students of GIS, spatial statistics, quantitative geography, and ecology … Essential reading for beginning students as well as those who wish to refresh their knowledge with respect to newer tools such as geographically weighted regression and spatial econometrics … introduces spatial analysis to those with very little training in statistics while at the same time developing applications using standard software for spatial analysis based on the ArcGIS and Geoda software systems. An excellent primer for anyone following a full course in spatial analysis. Spatial analysis is a tough subject to teach but Grekousis guides the reader through the basic ideas about understanding how correlations define our geographic world, introducing the full range of spatial tools and models.' Michael Batty, Centre for Advanced Spatial Analysis (CASA), University College London'Highly valuable and timely book for multidisciplinary professionals and students who aim to work with spatial problems but do not yet have the tools to study and solve these. The book provides an excellent introduction to the concepts and tools to think and analyse spatially, complemented by practical, realistic examples of how to apply this knowledge. The book has sufficient depth and rigor to allow students at all levels to learn for themselves and reach a good comprehension of a wide variety of aspects within this scientific domain.' Walter T. de Vries, Technical University of Munich'… an excellent blend of key theoretical concepts and applications. It covers a wide range of spatial topics and concepts while progressively building up in difficulty. The engaging examples, demonstrative code and laboratory follow-up exercises make this book suitable for both self-learners and traditional academic settings. Highly recommended.' Giorgos Mountrakis, State University of New York'A much welcomed and timely addition to the bookshelf of practitioners interested in the quantitative analysis of geographical data. The book offers a clear and concise exposition to basic and advanced methods and tools of spatial analysis, solidifying understanding through worked real-world case studies based on state-of-the-art commercial (ArcGIS) and public-domain (GeoDA) software. Definitely a book to be routinely used as a reference on the practical implementation of key analytical methods by people employing geographical data across a wide spectrum of disciplines.' Phaedon Kyriakidis, Cyprus University of TechnologyTable of Contents1. Think spatially: basic concepts of spatial analysis and space conceptualization; 2. Exploratory spatial data analysis tools and statistics; 3. Analyzing geographic distributions and point patterns; 4. Spatial autocorrelation; 5. Multivariate data in geography: data reduction and clustering; 6. Modeling relationships: regression and geographically weighted regression; 7. Spatial econometrics.

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Book SynopsisAvailable for the first time in McGraw-Hill''s Connect! Principles of Statistics for Engineers and Scientists emphasizes statistical methods and how they can be applied to problems in science and engineering. The book contains many examples that feature real, contemporary data sets, both to motivate students and to show connections to industry and scientific research. Because statistical analyses are done on computers, the book contains exercises and examples that involve interpreting, as well as generating, computer output. This book may be used effectively with any software package.Table of Contents1 Summarizing Univariate Data2 Summarizing Bivariate Data3 Probability4 Commonly Used Distributions5 Point and Interval Estimation for a Single Sample6 Hypothesis Tests for a Single Sample7 Inferences for Two Samples8 Inference in Linear Models9 Factorial Experiments10 Statistical Quality Control

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Book SynopsisGrid generation deals with the use of grids (meshes) in the numerical solution of partial differential equations by finite elements, finite volume, finite differences and boundary elements. Grid generation is applied in the aerospace, mechanical engineering and scientific computing fields. This book presents new research in the field.

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Book SynopsisCryptography is the study of methods to transform information from its original comprehensible form into a scrambled incomprehensible form, such that its content can only be disclosed to some qualified persons. In the past, cryptography helped ensure secrecy in important communications, such as those of spies, military leaders, and diplomats. In recent decades, it has expanded in two main ways: firstly, it provides mechanisms for more than just keeping secrets through schemes like digital signatures, digital cash, etc; secondly, cryptography is used by almost all computer users as it is embedded into the infrastructure for computing and telecommunications. Cryptography ensures secure communications through confidentiality, integrity, authenticity and non-repudiation. Cryptography has evolved over the years from Julius Cesar''s cipher, which simply shifts the letters of the words a fixed number of times, to the sophisticated RSA algorithm, which was invented by Ronald L. Rivest, Adi Shamir and Leonard M. Adleman, and the elegant AES cipher (Advanced Encryption Standard), which was invented by Joan Daemen and Vincent Rijmen. The need for fast but secure cryptographic systems is growing bigger. Therefore, dedicated hardware for cryptography is becoming a key issue for designers. With the spread of reconfigurable hardware such as FPGAs, embedded cryptographic hardware became cost-effective. Nevertheless, it is worthy to note that nowadays, even hardwired cryptographic algorithms are not safe. Attacks based on power consumption and electromagnetic Analysis, such as SPA, DPA and EMA have been successfully used to retrieve secret information stored in cryptographic devices. Besides performance in terms of area and throughput, designer of embedded cryptographic hardware must worry about the leakage of their implementations. The content of this book is divided into three main parts, which are focused on new trends in cryptographic hardware, arithmetic and factoring.

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Book SynopsisResearch and development in scientific computing and computational science has considerably increased the power of numerical simulation. Engineers and researchers are now able to solve large and complex problems which were impossible to solve in the past. This new book presents some techniques, methods and algorithms for solving engineering problems arising in energy and environment applications.

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Book SynopsisIsaac is a Department for Education project at the University of Cambridge that develops understanding and confidence through problem solving in the physical sciences, by combining accessible and concise print resources with a state of the art online study tool. This book is a co-publication between Periphyseos Press/Isaac and Cambridge University Press. ESSENTIAL PRE-UNIVERSITY MATHEMATICS FOR SCIENCES helps you master mathematics for final-year school courses and entry-level university and the maths needed for physics, chemistry and other sciences. Chapters 1?6 cover essential techniques in mathematics for sciences, offering both core practice and more advanced problems. Chapter 7 covers mathematical approaches to scientific problems, including population, money, nuclear chain reactions and random walks in gases and galaxies?each illustrating the power of mathematics. All problems can be answered on the Isaac online platform. Registration is free and gives both students and teachers personalised support through a sophisticated online marking system for all problems.

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Book Synopsis Scientific Computing for Scientists and Engineers is designed to teach undergraduate students relevant numerical methods and required fundamentals in scientific computing. Most problems in science and engineering require the solution of mathematical problems, most of which can only be done on a computer. Accurately approximating those problems requires solving differential equations and linear systems with millions of unknowns, and smart algorithms can be used on computers to reduce calculation times from years to minutes or even seconds. This book explains: How can we approximate these important mathematical processes? How accurate are our approximations? How efficient are our approximations? Scientific Computing for Scientists and Engineers covers: An introduction to a wide range of numerical methods for linear systems, eigenvalue problems, differential equations, numerical integration, and nonlinear problems; Scientific computing fundamentals like floating point representation of numbers and convergence; Analysis of accuracy and efficiency; Simple programming examples in MATLAB to illustrate the algorithms and to solve real life problems; Exercises to reinforce all topics.

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Book SynopsisThis book presents the statistical methods that are useful in the study of molecular evolution and illustrates how to use them in actual data analysis. Molecular evolution has been developing at a great pace over the past decade or so, driven by the huge increase in genetic sequence data from many organisms, the improvement of high-speed microcomputers, and the development of several new methods for phylogenetic analysis. This book for graduate students and researchers, assuming a basic knowledge of evolution, molecular biology, and elementary statistics, should make it possible for many investigators to incorporate refined statistical analysis of large-scale data in their own work. Nei is one of the leading workers in this area. He and Kumar have developed a computer program called MEGA, which has been sold for about $20 to over 1900 users. For the book, the authors are thoroughly revising MEGA and will make it available via FTP. The book also included analysis using the other most poTrade ReviewIt is worth its price * Plant Systematics and Evolution *Table of Contents1. Molecular basis of evolution ; 2. Evolutionary changes of amino acid sequences ; 3. Evolutionary changes of DNA sequences ; 4. Synonymous and nonsynonymous nucleotide substitutions ; 5. Phylogenetic trees ; 6. Phylogenetic inference: Distance methods ; 7. Phylogenetic inference: Maximum parsimony methods ; 8. Phylogenetic inference: Maximum likelihood methods ; 9. Accuracies and statistical tests of phylogenetic trees ; 10. Molecular clocks and linearized trees ; 11. Ancestral nucleotide and amino acid sequences ; 12. Genetic polymorphism and evolution ; 13. Population trees from genetic markers ; 14. Perspectives ; Appendices ; A. Mathematical sumbols and notations ; B. Geological timescale ; C. Geological events in the Cenozoic and Meszoic eras ; D. Evolution of organisms based on the fossil record

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Book SynopsisThis title is an introduction to the mathematical analysis of p- and hp-finite elements applied to elliptic problems in solid and fluid mechanics, and is suitable for graduate students and researchers who have had some prior exposure to finite element methods (FEM).Trade Review'Summarizing the book is the first theoretical book addressing the hp-version of the finite element method which is used today in practical computations. It is very well written and gives a very good review of the techniques and results in this relatively new direction in the FEM. It is highly recommended to anybody with mathematical interest for both learning and reference' ZAMMTable of ContentsVariational formulation of boundary value problems ; The Finite Element Method (FEM): definition, basic properties ; hp- Finite Elements in one dimension ; hp- Finite Elements in two dimensions ; Finite Element analysis of saddle point problems, mixed hp-FEM in incompressible fluid flow ; hp-FEM in the theory of elasticity

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Book SynopsisThis is an introduction to the mathematical foundations of quantum field theory, using operator algebraic methods and emphasizing the link between the mathematical formulations and related physical concepts. It starts with a general probabilistic description of physics, which encompasses both classical and quantum physics. The basic key physical notions are clarified at this point. It then introduces operator algebraic methods for quantum theory, and goes on to discuss the theory of special relativity, scattering theory, and sector theory in this context.Trade Review'the self-contained monograph provides an introduction suitable for mathematics graduates to the basic properties of quantum fields' AslibTable of ContentsStates and observables ; Quantum theory ; The relativistic symmetry ; Local observables ; Scattering theory ; Sector theory ; Appendix A: Hilbert space and operators ; Appendix B: Operator algebras ; Appendix C: Free fields

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Book SynopsisThis monograph provides a treatment of the theory of algebraic Riccati equations, an area of increasing interest in the mathematics and engineering communities. A range of applications are covered, demonstrating the use of these equations for providing solutions to complex problems.Table of Contents1. Preliminaries from the theory of matrices ; 2. Indefinite scalar products ; 3. Skew-symmetric scalar products ; 4. Matrix theory and control ; 5. Linear matrix equations ; 6. Rational matrix functions ; 7. Geometric theory: the complex case ; 8. Geometric theory: the real case ; 9. Constructive existence and comparison theorems ; 10. Hermitian solutions and factorizations of rational matrix functions ; 11. Perturbation theory ; 12. Geometric theory for the discrete algebraic Riccati equation ; 13. Constructive existence and comparison theorems ; 14. Perturbation theory for discrete algebraic Riccati equations ; 15. Discrete algebraic Riccati equations and matrix pencils ; 16. Linear-quadratic regulator problems ; 17. The discrete Kalman filter ; 18. The total least squares technique ; 19. Canonical factorization ; 20. Hoo control problems ; 21. Contractive rational matrix functions ; 22. The matrix sign function ; 23. Structured stability radius ; Bibliography ; List of notations ; Index

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Book SynopsisMethods in theoretical quantum optics is aimed at those readers who already have some knowledge of mathematical methods and have also been introduced to the basic ideas of quantum optics. This book is ideal for students who have already explored the basics of the quantum theory of light and are seeking to acquire the mathematical skills used in real problems. This book is not primarily about the physics of quantum optics, but rather presents the mathematical methods widely used by workers in this field. There is no comparable book which covers either the range or the depth of mathematical techniques.Trade Review... many valuable insights ... Even old hands at the quantum optics game will benefit from these ... The authors cover a very wide range of material appropriate to quantum optics. By bringing it together in the way they have, they have made an important contribution to the teaching and understanding of quantum optics. * Zentralblatt MATH *Table of Contents1. Foundations ; 2. Coherent interactions ; 3. Operators and states ; 4. Quantum statistics of fields ; 5. Dissipative processes ; 6. Dressed states ; Appendices ; Selected bibliography ; Index ; 1. Foundations ; 2. Coherent interactions ; 3. Operators and states ; 4. Quantum statistics of fields ; 5. Dissipative processes ; 6. Dressed states ; Appendices ; Selected bibliography ; Index

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Book SynopsisDrawing on the author's extensive experience of supporting students undertaking projects, Scientific Data Analysis is a guide for any science undergraduate or beginning graduate who needs to analyse their own data, and wants a clear, step-by-step description of how to carry out their analysis in a robust, error-free way.Trade ReviewThis is an appealing introduction that would be accessible to a variety of students at the college level. Its strengths are clarity and directness with an abundance of good examples and case studies. * MAA Review *Table of ContentsPART I - UNDERSTANDING THE STATISTICS; PART II - ANALYSING EXPERIMENTAL DATA

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Book SynopsisThis new edition updates Durbin & Koopman''s important text on the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. Additions to this second edition include the filtering of nonlinear and non-Gaussian series.Part I of the book obtains the mean and variance of the state, of a variable intended to measure the effect of an interaction and of regression coefficients, in terms of the observations.Part II extends the treatment to nonlinear and non-normal models. For these, analytical solutions are not available so methods are based on simulation.Trade ReviewReview from previous edition ...provides an up-to-date exposition and comprehensive treatment of state space models in time series analysis...This book will be helpful to graduate students and applied statisticians working in the area of econometric modelling as well as researchers in the areas of engineering, medicine and biology where state space models are used. * Journal of the Royal Statistical Society *Table of ContentsPART I: THE LINEAR STATE SPACE MODEL; PART II: NON-GAUSSIAN AND NONLINEAR STATE SPACE MODELS

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisSince the 17th century, physical theories have been expressed in the language of mathematical equations. This introduction to quantum theory uses that language to enable the reader to comprehend the notoriously non-intuitive ideas of quantum physics. The mathematical knowledge needed for using this book comes from standard undergraduate mathematics courses and is described in detail in the section Prerequisites. This text is especially aimed at advanced undergraduate and graduate students of mathematics, computer science, engineering and chemistry among other disciplines, provided they have the math background even though lacking preparation in physics. In fact, no previous formal study of physics is assumed.Trade Review“The target audience is ‘advanced undergraduate mathematics students who had no or only very little prior knowledge of physics’. It would indeed be a rare variety of mathematics advanced undergraduates who would fit this bill. … an interesting supplement for students with a mathematical bent.” (Amitava Raychaudhuri, zbMATH 1458.81002, 2021)Table of ContentsIntroduction to this Path.- Viewpoint.- Neither Particle nor Wave.- Schrödinger's Equation.- Operators and Canonical Quantization.- The Harmonic Oscillator.- Interpreting: Mathematics.- Interpreting: Physics.- The Language of Hilbert Space.- Interpreting: Measurement.- The Hydrogen Atom.- Angular Momentum.- The Rotation Group SO(3).- Spin and SU(2).- Bosons and Fermions.- Classical and Quantum Probability.- The Heisenberg Picture.- Uncertainty (Optional).- Speaking of Quantum Theory (Optional).- Complementarity (Optional).- Axioms (Optional).- And Gravity?.- Measure Theory: A Crash Course.

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Book SynopsisThis easy-to-understand textbook introduces the mathematical language and problem-solving tools essential to anyone wishing to enter the world of computer and information sciences. Specifically designed for the student who is intimidated by mathematics, the book offers a concise treatment in an engaging style.The thoroughly revised third edition features a new chapter on relevance-sensitivity in logical reasoning and many additional explanations on points that students find puzzling, including the rationale for various shorthand ways of speaking and ‘abuses of language’ that are convenient but can give rise to misunderstandings. Solutions are now also provided for all exercises.Topics and features: presents an intuitive approach, emphasizing how finite mathematics supplies a valuable language for thinking about computation; discusses sets and the mathematical objects built with them, such as relations and functions, as well as recursion and induction; introduces core topics of mathematics, including combinatorics and finite probability, along with the structures known as trees; examines propositional and quantificational logic, how to build complex proofs from simple ones, and how to ensure relevance in logic; addresses questions that students find puzzling but may have difficulty articulating, through entertaining conversations between Alice and the Mad Hatter; provides an extensive set of solved exercises throughout the text.This clearly-written textbook offers invaluable guidance to students beginning an undergraduate degree in computer science. The coverage is also suitable for courses on formal methods offered to those studying mathematics, philosophy, linguistics, economics, and political science. Assuming only minimal mathematical background, it is ideal for both the classroom and independent study.Table of ContentsPart I: Sets Collecting Things Together: Sets Comparing Things: Relations Associating One Item with Another: Functions Recycling Outputs as Inputs: Induction and Recursion Part II: Math Counting Things: Combinatorics Weighing the Odds: Probability Squirrel Math: Trees Part III: Logic Yea and Nay: Propositional Logic Something about Everything: Quantificational Logic Just Supposing: Proof and Consequence Sticking to the Point: Relevance in Logic

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Book SynopsisThis book is of interest to researchers wanting to know more about the latest topics and methods in the fields of the kinematics, control and design of robotic systems. The papers cover the full range of robotic systems, including serial, parallel and cable-driven manipulators. The systems range from being less than fully mobile, to kinematically redundant, to over-constrained. The book brings together 43 peer-reviewed papers. They report on the latest scientific and applied achievements. The main theme that connects them is the movement of robots in the most diverse areas of application.Table of ContentsAdvances in Robot Kinematics Facts and Thoughts.- Inverse Kinematics Using a Converging Paths Algorithm.- Design parameters influence on the static workspace and the stiffness range of a tensegrity mechanism.- Bennett Based Balanced Butterfly Linkage, Deployable Linkage with Inherent Balance.- A Compliant Linkage for Cooperative Object Manipulation through a Heterogeneous Mobile Multi-Robot System.- Modeling and Control of a Redundant Tensegrity-based Manipulator.

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Book SynopsisAll engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally “analog” disciplines—mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates their specific value to engineers by presenting concrete examples based on physical systems. The book proceeds from basic learning models to deep neural networks, gradually increasing readers’ ability to apply modern machine learning techniques to their current work and to prepare them for future, as yet unknown, problems. Rather than taking a black box approach, the author teaches a broad range of techniques while conveying the kinds of problems best addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries scikit-learn and tensorflow, demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally benefits undergraduate engineering students who wish to acquire the skills required by future employers, and practicing engineers who wish to expand and update their problem-solving toolkit.Table of ContentsPart I Fundamentals 1.0 Introduction 1.1. Where machine learning can help engineers 1.2. Where machine learning cannot help engineers 1.3. Machine learning to correct idealized models 2. The Landscape of machine learning 2.1. Supervised learning 2.1.1. Regression 2.1.2. Classification 2.1.3. Time series 2.1.4. Reinforcement 2.2. Unsupervised Learning 2.3. Optimization 2.4. Bayesian statistics 2.5. Cross-validation 3. Linear Models 3.1. Linear regression 3.2. Logistic regression 3.3. Regularized regression 3.4. Case Study: Determining physical laws using regularized regression 4. Tree-Based Models 4.1. Decision Trees 4.2. Random Forests 4.3. BART 4.4. Case Study: Modeling an experiment using random forest models 5. Clustering data 5.1. Singular value decomposition 5.2. Case Study: SVD to standardize several time series 5.3. K-means 5.4. K-nearest neighbors 5.5. t-SNE 5.6. Case Study: The reflectance spectrum of different foliage Part II Deep Neural Networks 6. Feed-Forward Neural Networks 6.1. Neurons 6.2. Dropout 6.3. Backpropagation 6.4. Initialization 6.5. Regression 6.6. Classification 6.7. Case Study: The strength of concrete as a function of age and ingredients 7. Convolutional Neural Networks 7.1. Convolutions 7.2. Pooling 7.3. Residual networks 7.4. Case Study: Finding volcanoes on Venus 8. Recurrent neural networks for time series data 8.1. Basic Recurrent neural networks 8.2. Long-term, Short-Term memory 8.3. Attention networks 8.4. Case Study: Predicting future system performance Part III Advanced Topics in Machine Learning 9. Unsupervised Learning with Neural Networks 9.1. Auto-encoders 9.2. Boltzmann machines 9.3. Case study: Optimization using Inverse models 10. Reinforcement learning 10.1. Case study: controlling a mechanical gantry 11. Transfer learning 11.1. Case study: Transfer learning a simulation emulator for experimental measurements Part IV Appendices A. SciKit-Learn B. Tensorflow

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Book SynopsisAll engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally “analog” disciplines—mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates their specific value to engineers by presenting concrete examples based on physical systems. The book proceeds from basic learning models to deep neural networks, gradually increasing readers’ ability to apply modern machine learning techniques to their current work and to prepare them for future, as yet unknown, problems. Rather than taking a black box approach, the author teaches a broad range of techniques while conveying the kinds of problems best addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries scikit-learn and tensorflow, demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally benefits undergraduate engineering students who wish to acquire the skills required by future employers, and practicing engineers who wish to expand and update their problem-solving toolkit.Table of ContentsPart I Fundamentals 1.0 Introduction 1.1. Where machine learning can help engineers 1.2. Where machine learning cannot help engineers 1.3. Machine learning to correct idealized models 2. The Landscape of machine learning 2.1. Supervised learning 2.1.1. Regression 2.1.2. Classification 2.1.3. Time series 2.1.4. Reinforcement 2.2. Unsupervised Learning 2.3. Optimization 2.4. Bayesian statistics 2.5. Cross-validation 3. Linear Models 3.1. Linear regression 3.2. Logistic regression 3.3. Regularized regression 3.4. Case Study: Determining physical laws using regularized regression 4. Tree-Based Models 4.1. Decision Trees 4.2. Random Forests 4.3. BART 4.4. Case Study: Modeling an experiment using random forest models 5. Clustering data 5.1. Singular value decomposition 5.2. Case Study: SVD to standardize several time series 5.3. K-means 5.4. K-nearest neighbors 5.5. t-SNE 5.6. Case Study: The reflectance spectrum of different foliage Part II Deep Neural Networks 6. Feed-Forward Neural Networks 6.1. Neurons 6.2. Dropout 6.3. Backpropagation 6.4. Initialization 6.5. Regression 6.6. Classification 6.7. Case Study: The strength of concrete as a function of age and ingredients 7. Convolutional Neural Networks 7.1. Convolutions 7.2. Pooling 7.3. Residual networks 7.4. Case Study: Finding volcanoes on Venus 8. Recurrent neural networks for time series data 8.1. Basic Recurrent neural networks 8.2. Long-term, Short-Term memory 8.3. Attention networks 8.4. Case Study: Predicting future system performance Part III Advanced Topics in Machine Learning 9. Unsupervised Learning with Neural Networks 9.1. Auto-encoders 9.2. Boltzmann machines 9.3. Case study: Optimization using Inverse models 10. Reinforcement learning 10.1. Case study: controlling a mechanical gantry 11. Transfer learning 11.1. Case study: Transfer learning a simulation emulator for experimental measurements Part IV Appendices A. SciKit-Learn B. Tensorflow

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Book SynopsisData Management for Natural Scientists offers a practical guide for scientific processing of data. It covers the way from “getting hands on” experimental results to ensuring their use for addressing various scientific questions. Code snippets are provided in order to introduce the proposed workstream and to demonstrate the adjustability to specific challenges.

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Book SynopsisWritten by an experienced physicist who is active in applying computer algebra to relativistic astrophysics and education, this is the resource for mathematical methods in physics using MapleTM and MathematicaTM. Through in-depth problems from core courses in the physics curriculum, the author guides students to apply analytical and numerical techniques in mathematical physics, and present the results in interactive graphics. Around 180 simulating exercises are included to facilitate learning by examples. This book is a must-have for students of physics, electrical and mechanical engineering, materials scientists, lecturers in physics, and university libraries. * Free online MapleTM material at http://www.wiley-vch.de/templates/pdf/maplephysics.zip * Free online MathematicaTM material at http://www.wiley-vch.de/templates/pdf/physicswithmathematica.zip * Solutions manual for lecturers available at www.wiley-vch.de/supplements/Table of Contents1. Introduction 2. Oscillatory Motion 3. Calculus of Variations 4. Integration of Equations of Motion 5. Orthogonal Functions and Expansions 6. Electrostatics 7. Boundary-Value Problems 8. Magnetostatics 9. Electric Circuits 10. Waves 11. Physical Optics 12. Special Relativity 13. Quantum Phenomena 14. Schrodinger Equation in One Dimension I 15. Schrodinger Equation in One Dimension II 16. Schrodinger Equation in Three Dimensions 17. Quantum Statistics 18. General Relativity A1 Physical and Astrophysical Constants A2 Mathematical Notes

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Book SynopsisThe book introduces most of the basic tools of chemometrics including experimental design, signal analysis, statistical methods for analytical chemistry and multivariate methods.Trade Review"…useful for introducing chemometrics in undergraduate classes…a valuable encyclopedia for researchers…" (Journal of Chemical Education, December 2007)Table of ContentsPreface. 1 Introduction. 1.1 Development of Chemometrics. 1.2 Application Areas. 1.3 How to Use this Book. 1.4 Literature and Other Sources of Information. References. 2 Experimental Design. 2.1 Why Design Experiments in Chemistry? 2.2 Degrees of Freedom and Sources of Error. 2.3 Analysis of Variance and Interpretation of Errors. 2.4 Matrices, Vectors and the Pseudoinverse. 2.5 Design Matrices. 2.6 Factorial Designs. 2.7 An Example of a Factorial Design. 2.8 Fractional Factorial Designs. 2.9 Plackett–Burman and Taguchi Designs. 2.10 The Application of a Plackett–Burman Design to the Screening of Factors Influencing a Chemical Reaction. 2.11 Central Composite Designs. 2.12 Mixture Designs. 2.13 A Four Component Mixture Design Used to Study Blending of Olive Oils. 2.14 Simplex Optimization. 2.15 Leverage and Confidence in Models. 2.16 Designs for Multivariate Calibration. References. 3 Statistical Concepts. 3.1 Statistics for Chemists. 3.2 Errors. 3.3 Describing Data. 3.4 The Normal Distribution. 3.5 Is a Distribution Normal? 3.6 Hypothesis Tests. 3.7 Comparison of Means: the t-Test. 3.8 F-Test for Comparison of Variances. 3.9 Confidence in Linear Regression. 3.10 More about Confidence. 3.11 Consequences of Outliers and How to Deal with Them. 3.12 Detection of Outliers. 3.13 Shewhart Charts. 3.14 More about Control Charts. References. 4 Sequential Methods. 4.1 Sequential Data. 4.2 Correlograms. 4.3 Linear Smoothing Functions and Filters. 4.4 Fourier Transforms. 4.5 Maximum Entropy and Bayesian Methods. 4.6 Fourier Filters. 4.7 Peakshapes in Chromatography and Spectroscopy. 4.8 Derivatives in Spectroscopy and Chromatography. 4.9 Wavelets. References. 5 Pattern Recognition. 5.1 Introduction. 5.2 Principal Components Analysis. 5.3 Graphical Representation of Scores and Loadings. 5.4 Comparing Multivariate Patterns. 5.5 Preprocessing. 5.6 Unsupervised Pattern Recognition: Cluster Analysis. 5.7 Supervised Pattern Recognition. 5.8 Statistical Classification Techniques. 5.9 K Nearest Neighbour Method. 5.10 How Many Components Characterize a Dataset? 5.11 Multiway Pattern Recognition. References. 6 Calibration. 6.1 Introduction. 6.2 Univariate Calibration. 6.3 Multivariate Calibration and the Spectroscopy of Mixtures. 6.4 Multiple Linear Regression. 6.5 Principal Components Regression. 6.6 Partial Least Squares. 6.7 How Good is the Calibration and What is the Most Appropriate Model? 6.8 Multiway Calibration. References. 7 Coupled Chromatography. 7.1 Introduction. 7.2 Preparing the Data. 7.3 Chemical Composition of Sequential Data. 7.4 Univariate Purity Curves. 7.5 Similarity Based Methods. 7.6 Evolving and Window Factor Analysis. 7.7 Derivative Based Methods. 7.8 Deconvolution of Evolutionary Signals. 7.9 Noniterative Methods for Resolution. 7.10 Iterative Methods for Resolution. 8 Equilibria, Reactions and Process Analytics. 8.1 The Study of Equilibria using Spectroscopy. 8.2 Spectroscopic Monitoring of Reactions. 8.3 Kinetics and Multivariate Models for the Quantitative Study of Reactions 8.4 Developments in the Analysis of Reactions using On-line Spectroscopy. 8.5 The Process Analytical Technology Initiative. References. 9 Improving Yields and Processes Using Experimental Designs. 9.1 Introduction. 9.2 Use of Statistical Designs for Improving the Performance of Synthetic Reactions. 9.3 Screening for Factors that Influence the Performance of a Reaction. 9.4 Optimizing the Process Variables. 9.5 Handling Mixture Variables using Simplex Designs. 9.6 More about Mixture Variables. 10 Biological and Medical Applications of Chemometrics. 10.1 Introduction. 10.2 Taxonomy. 10.3 Discrimination. 10.4 Mahalanobis Distance. 10.5 Bayesian Methods and Contingency Tables. 10.6 Support Vector Machines. 10.7 Discriminant Partial Least Squares. 10.8 Micro-organisms. 10.9 Medical Diagnosis using Spectroscopy. 10.10 Metabolomics using Coupled Chromatography and Nuclear Magnetic Resonance. References. 11 Biological Macromolecules. 11.1 Introduction. 11.2 Sequence Alignment and Scoring Matches. 11.3 Sequence Similarity. 11.4 Tree Diagrams. 11.5 Phylogenetic Trees. References. 12 Multivariate Image Analysis. 12.1 Introduction. 12.2 Scaling Images. 12.3 Filtering and Smoothing the Image. 12.4 Principal Components for the Enhancement of Images. 12.5 Regression of Images. 12.6 Alternating Least Squares as Employed in Image Analysis. 12.7 Multiway Methods In Image Analysis. References. 13 Food. 13.1 Introduction. 13.2 How to Determine the Origin of a Food Product using Chromatography. 13.3 Near Infrared Spectroscopy. 13.4 Other Information. 13.5 Sensory Analysis: Linking Composition to Properties. 13.6 Varimax Rotation. 13.7 Calibrating Sensory Descriptors to Composition. References. Index.

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Book SynopsisThe objective of this book is to motivate an appreciation of contemporary statistical techniques within the context of engineering. The author presents an optimum blend between statistical thinking and statistical methodology through emphasis of a broad sweep of tools rather than endless streams of seemingly unrelated methods and formulae.Trade Review"Overall this is an excellent book, which defines a broader mandate than many of its competing texts. By providing, clear, understandable discussion of the basics of statistics through to more advanced methods commonly used by engineers, this book is an essential reference for practitioners, and an ideal text for a two semester course introducing engineers to the power and utility of statistics." (The American Statistician, August 2008) "In this book on modern engineering statistics, Ryan does an excellent job of providing the appropriate statistical concepts and tools using engineering resources.... Highly recommended. Lower- and upper-division undergraduates" (CHOICE, April 2008) "This self-contained volume motivates an appreciation of statistical techniques within the context of engineering; many datasets that are used in the chapters and exercises are from engineering sources. This book is ideal for either a one- or two-semester course in engineering statistics." (Computing Reviews, April 2008)Table of ContentsPreface xvii 1. Methods of Collecting and Presenting Data 1 1.1 Observational Data and Data from Designed Experiments 3 1.2 Populations and Samples 5 1.3 Variables 6 1.4 Methods of Displaying Small Data Sets 7 1.5 Methods of Displaying Large Data Sets 16 1.6 Outliers 22 1.7 Other Methods 22 1.8 Extremely Large Data Sets: Data Mining 23 1.9 Graphical Methods: Recommendations 23 1.10 Summary 24 References 24 Exercises 25 2. Measures of Location and Dispersion 45 2.1 Estimating Location Parameters 46 2.2 Estimating Dispersion Parameters 50 2.3 Estimating Parameters from Grouped Data 55 2.4 Estimates from a Boxplot 57 2.5 Computing Sample Statistics with MINITAB 58 2.6 Summary 58 Reference 58 Exercises 58 3. Probability and Common Probability Distributions 68 3.1 Probability: From the Ethereal to the Concrete 68 3.3 Common Discrete Distributions 76 3.4 Common Continuous Distributions 92 3.5 General Distribution Fitting 106 3.6 How to Select a Distribution 107 3.7 Summary 108 References 109 Exercises 109 4. Point Estimation 121 4.1 Point Estimators and Point Estimates 121 4.2 Desirable Properties of Point Estimators 121 4.3 Distributions of Sampling Statistics 125 4.4 Methods of Obtaining Estimators 128 4.5 Estimating σθ 132 4.6 Estimating Parameters Without Data 133 4.7 Summary 133 References 134 Exercises 134 5. Confidence Intervals and Hypothesis Tests—One Sample 140 5.1 Confidence Interval for μ: Normal Distribution σ Not Estimated from Sample Data 140 5.2 Confidence Interval for μ: Normal Distribution σ Estimated from Sample Data 146 5.3 Hypothesis Tests for μ: Using Z and t 147 5.4 Confidence Intervals and Hypothesis Tests for a Proportion 157 5.5 Confidence Intervals and Hypothesis Tests for σ2 and σ 161 5.6 Confidence Intervals and Hypothesis Tests for the Poisson Mean 164 5.7 Confidence Intervals and Hypothesis Tests When Standard Error Expressions are Not Available 166 5.8 Type I and Type II Errors 168 5.9 Practical Significance and Narrow Intervals: The Role of n 172 5.10 Other Types of Confidence Intervals 173 5.11 Abstract of Main Procedures 174 5.12 Summary 175 Appendix: Derivation 176 References 176 Exercises 177 6. Confidence Intervals and Hypothesis Tests—Two Samples 189 6.1 Confidence Intervals and Hypothesis Tests for Means: Independent Samples 189 6.2 Confidence Intervals and Hypothesis Tests for Means: Dependent Samples 197 6.3 Confidence Intervals and Hypothesis Tests for Two Proportions 200 6.4 Confidence Intervals and Hypothesis Tests for Two Variances 202 6.5 Abstract of Procedures 204 6.6 Summary 205 References 205 Exercises 205 7. Tolerance Intervals and Prediction Intervals 214 7.1 Tolerance Intervals: Normality Assumed 215 7.2 Tolerance Intervals and Six Sigma 219 7.3 Distribution-Free Tolerance Intervals 219 7.4 Prediction Intervals 221 7.5 Choice Between Intervals 227 7.6 Summary 227 References 228 Exercises 229 8. Simple Linear Regression Correlation and Calibration 232 8.1 Introduction 232 8.2 Simple Linear Regression 232 8.3 Correlation 254 8.4 Miscellaneous Uses of Regression 256 8.5 Summary 264 References 264 Exercises 265 9. Multiple Regression 276 9.1 How Do We Start? 277 9.2 Interpreting Regression Coefficients 278 9.3 Example with Fixed Regressors 279 9.4 Example with Random Regressors 281 9.5 Example of Section 8.2.4 Extended 291 9.6 Selecting Regression Variables 293 9.7 Transformations 299 9.8 Indicator Variables 300 9.9 Regression Graphics 300 9.10 Logistic Regression and Nonlinear Regression Models 301 9.11 Regression with Matrix Algebra 302 9.12 Summary 302 References 303 Exercises 304 10. Mechanistic Models 314 10.1 Mechanistic Models 315 10.2 Empirical–Mechanistic Models 316 10.3 Additional Examples 324 10.4 Software 325 10.5 Summary 326 References 326 Exercises 327 11. Control Charts and Quality Improvement 330 11.1 Basic Control Chart Principles 330 11.2 Stages of Control Chart Usage 331 11.3 Assumptions and Methods of Determining Control Limits 334 11.4 Control Chart Properties 335 11.5 Types of Charts 336 11.6 Shewhart Charts for Controlling a Process Mean and Variability (Without Subgrouping) 336 11.7 Shewhart Charts for Controlling a Process Mean and Variability (With Subgrouping) 344 11.8 Important Use of Control Charts for Measurement Data 349 11.9 Shewhart Control Charts for Nonconformities and Nonconforming Units 349 11.10 Alternatives to Shewhart Charts 356 11.11 Finding Assignable Causes 359 11.12 Multivariate Charts 362 11.13 Case Study 362 11.14 Engineering Process Control 364 11.15 Process Capability 365 11.16 Improving Quality with Designed Experiments 366 11.17 Six Sigma 367 11.18 Acceptance Sampling 368 11.19 Measurement Error 368 11.20 Summary 368 References 369 Exercises 370 12. Design and Analysis of Experiments 382 12.1 Processes Must be in Statistical Control 383 12.2 One-Factor Experiments 384 12.3 One Treatment Factor and at Least One Blocking Factor 392 12.4 More Than One Factor 395 12.5 Factorial Designs 396 12.6 Crossed and Nested Designs 405 12.7 Fixed and Random Factors 406 12.8 ANOM for Factorial Designs 407 12.9 Fractional Factorials 409 12.10 Split-Plot Designs 413 12.11 Response Surface Designs 414 12.12 Raw Form Analysis Versus Coded Form Analysis 415 12.13 Supersaturated Designs 416 12.14 Hard-to-Change Factors 416 12.15 One-Factor-at-a-Time Designs 417 12.16 Multiple Responses 418 12.17 Taguchi Methods of Design 419 12.18 Multi-Vari Chart 420 12.19 Design of Experiments for Binary Data 420 12.20 Evolutionary Operation (EVOP) 421 12.21 Measurement Error 422 12.22 Analysis of Covariance 422 12.23 Summary of MINITAB and Design-Expert® Capabilities for Design of Experiments 422 12.24 Training for Experimental Design Use 423 12.25 Summary 423 Appendix A Computing Formulas 424 Appendix B Relationship Between Effect Estimates and Regression Coefficients 426 References 426 Exercises 428 13. Measurement System Appraisal 441 13.1 Terminology 442 13.2 Components of Measurement Variability 443 13.3 Graphical Methods 449 13.4 Bias and Calibration 449 13.5 Propagation of Error 454 13.6 Software 455 13.7 Summary 456 References 456 Exercises 457 14. Reliability Analysis and Life Testing 460 14.1 Basic Reliability Concepts 461 14.2 Nonrepairable and Repairable Populations 463 14.3 Accelerated Testing 463 14.4 Types of Reliability Data 466 14.5 Statistical Terms and Reliability Models 467 14.6 Reliability Engineering 473 14.7 Example 474 14.8 Improving Reliability with Designed Experiments 474 14.9 Confidence Intervals 477 14.10 Sample Size Determination 478 14.11 Reliability Growth and Demonstration Testing 479 14.12 Early Determination of Product Reliability 480 14.13 Software 480 14.14 Summary 481 References 481 Exercises 482 15. Analysis of Categorical Data 487 15.1 Contingency Tables 487 15.2 Design of Experiments: Categorical Response Variable 497 15.3 Goodness-of-Fit Tests 498 15.4 Summary 500 References 500 Exercises 501 16. Distribution-Free Procedures 507 16.1 Introduction 507 16.2 One-Sample Procedures 508 16.3 Two-Sample Procedures 512 16.4 Nonparametric Analysis of Variance 514 16.5 Exact Versus Approximate Tests 519 16.6 Nonparametric Regression 519 16.7 Nonparametric Prediction Intervals and Tolerance Intervals 521 16.8 Summary 521 References 521 Exercises 522 17. Tying It All Together 525 17.1 Review of Book 525 17.2 The Future 527 17.3 Engineering Applications of Statistical Methods 528 Reference 528 Exercises 528 Answers to Selected Excercises 533 Appendix: Statistical Tables 562 Table A Random Numbers 562 Table B Normal Distribution 564 Table C t-Distribution 566 Table D F-Distribution 567 Table E Factors for Calculating Two-Sided 99% Statistical Intervals for a Normal Population to Contain at Least 100p% of the Population 570 Table F Control Chart Constants 571 Author Index 573 Subject Index 579

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Book SynopsisMathematical Methods in Biology uniquely covers both deterministic and probabilistic models, including algorithms in the MATLAB platform. The book focuses mostly in one area of the life sciences, focusing mainly on theoretical ecology.Trade Review"Admirably, the volume is written with bits of MATLAB code inserted at appropriate places and has exercises interspersed throughout the text (as well as hints for solutions to the exercises at the end of the book)." The Quarterly Review of Biology, June 2010) "The mathematical and reasoning sophistication increases as the chapters proceed." (Book News, December 2009)Table of ContentsPreface. 1. Introduction To Ecological Modeling. 1.1 Mathematical Models. 1.2 Rates of Change. 1.3 Balance Laws. 1.4 Temperature in the Environment. 1.5 Dimensionless Variables. 1.6 Descriptive Statistics. 1.7 Regression and Curve Fitting. 1.8 Reference Notes. 2. Population Dynamics for Single Species. 2.1 Laws of Population Dynamics. 2.2 Continuous Time Models. 2.3 Qualitative Analysis of Population Models. 2.4 Dynamics of Predation. 2.5 Discrete Time Models. 2.6 Equilibria, Stability, and Chaos. 2.7 Reference Notes. 3. Structure and Interacting Populations. 3.1 Structure--Juveniles and Adults. 3.2 Structured Linear Models. 3.3 Nonlinear Interactions. 3.4 Appendix--Matrices. 3.5 Reference Notes. 4. Interactions in Continuous Time. 4.1 Interacting Populations. 4.2 Phase Plane Analysis. 4.3 Linear Systems. 4.4 Nonlinear Systems. 4.5 Bifurcation. 4.6 Reference Notes. 5. Concepts of Probability. 5.1 Introductory Examples and Definitions. 5.2 The Hardy-Weinberg Law. 5.3 Continuous Random Variables. 5.4 Discrete Random Variables. 5.5 Joint Probability Distributions. 5.6 Covariance and Correlation. 5.7 Reference Notes. 6. Statistical Inference. 6.1 Introduction. 6.2 Interval Analysis. 6.3 Estimating Proportions. 6.4 The Chi-Squared Test. 6.5 Hypothesis Testing. 6.6 Bootstrap Methods. 6.7 Reference Notes. 7. Stochastic Processes. 7.1 Introduction. 7.2 Randomizing Discrete Dynamics. 7.3 Random Walk. 7.4 Birth Processes. 7.5 Stochastic Differential Equations. 7.6 SDEs from Markov Models. 7.7 Solving SDEs. 7.8 The Fokker-Planck Equation. 7.9 Reference Notes. A. Hints and Solutions to Exercises

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Book SynopsisThis is a practical book on how to apply statistical methods successfully. The Authors have deliberately kept formulae to a minimum to enable the reader to concentrate on how to use the methods and to understand what the methods are for. Each method is introduced and used in a real situation from industry or research. Each chapter features situations based on the authors' experience and looks at statistical methods for analysing data and, where appropriate, discusses the assumptions of these methods. Key features: Provides a practical hands-on manual for workplace applications. Introduces a broad range of statistical methods from confidence intervals to trend analysis. Combines realistic case studies and examples with a practical approach to statistical analysis. Features examples drawn from a wide range of industries including chemicals, petrochemicals, nuclear power, food and pharmaceuticals. Includes a supporting Trade Review"Overall, the book could be a clear introduction to a set of useful tools either in self study or used as an aid for instruction for those with no previous exposure." (The American Statistician, 1 February 2011) Table of ContentsPreface. 1 Samples and populations. Introduction. What a lottery! No can do. Nobody is listening to me. How clean is my river? Discussion. 2 What is the true mean? Introduction. Presenting data. Averages. Measures of variability. Relative standard deviation . Degrees of freedom. Confidence interval for the population mean. Sample sizes. How much moisture is in the raw material? Problems. 3 Exploratory data analysis. Introduction. Histograms: is the process capable of meeting specifications? Box plots: how long before the lights go out? The box plot in practice. Problems. 4 Significance testing. Introduction. The one-sample t -test. The significance testing procedure. Confidence intervals as an alternative to significance testing. Confidence interval for the population standard deviation. F-test for ratio of standard deviations. Problems. 5 The normal distribution. Introduction. Properties of the normal distribution. Example. Setting the process mean. Checking for normality. Uses of the normal distribution. Problems. 6 Tolerance intervals. Introduction. Example. Confidence intervals and tolerance intervals. 7 Outliers. Introduction. Grubbs’ test. Warning. 8 Significance tests for comparing two means. Introduction. Example: watching paint lose its gloss. The two-sample t -test for independent samples. An alternative approach: a confidence intervals for the difference between population means. Sample size to estimate the difference between two means. A production example. Confidence intervals for the difference between the two suppliers. Sample size to estimate the difference between two means. Conclusions. Problems. 9 Significance tests for comparing paired measurements. Introduction. Comparing two fabrics. The wrong way. The paired sample t -test. Presenting the results of significance tests. One-sided significance tests. Problems. 10 Regression and correlation. Introduction. Obtaining the best straight line. Confidence intervals for the regression statistics. Extrapolation of the regression line. Correlation coefficient. Is there a significant relationship between the variables? How good a fit is the line to the data? Assumptions. Problems. 11 The binomial distribution. Introduction. Example. An exact binomial test. A quality assurance example. What is the effect of the batch size? Problems. 12 The Poisson distribution. Introduction. Fitting a Poisson distribution. Are the defects random? The Poisson distribution. Poisson dispersion test. Confidence intervals for a Poisson count. A significance test for two Poisson counts. How many black specks are in the batch? How many pathogens are there in the batch? Problems. 13 The chi-squared test for contingency tables. Introduction. Two-sample test for percentages. Comparing several percentages. Where are the differences? Assumptions. Problems. 14 Non-parametric statistics. Introduction. Descriptive statistics. A test for two independent samples: Wilcoxon–Mann–Whitney test. A test for paired data: Wilcoxon matched-pairs sign test. What type of data can be used? Example: cracking shoes. Problems. 15 Analysis of variance: Components of variability. Introduction. Overall variability. Analysis of variance. A practical example. Terminology. Calculations. Significance test. Variation less than chance? When should the above methods not be used? Between- and within-batch variability. How many batches and how many prawns should be sampled? Problems. 16 Cusum analysis for detecting process changes. Introduction. Analysing past data. Intensity. Localised standard deviation. Significance test. Yield. Conclusions from the analysis. Problem. 17 Rounding of results. Introduction. Choosing the rounding scale. Reporting purposes: deciding the amount of rounding. Reporting purposes: rounding of means and standard deviations. Recording the original data and using means and standard deviations in statistical analysis. References. Solutions to Problems. Statistical Tables. Index.

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Book SynopsisThe increase in the use of microarray technology has led to the need for good standards of microarray experimental notation, data representation, and the introduction of standard experimental controls, as well as standard data normalization and analysis techniques. This book covers the subject.Trade Review"I liked this book and would recommend it to any statistician new to microarray data analysis…a unique combination of features that make it a contender among the standard textbooks…" (Journal of the American Statistical Association, June 2006) "...clear...up-to-date...lively advice...an excellent reference text for any researcher interested in the analysis of transcriptomic data." (Short Book Reviews, Vol.25, No.1, April 2005) "...this is a very good introduction to one of the most widely used methods for assessing differential expression..." (Journal of the Royal Statistical Society, Vol 168 (4) 2005) "...presents a coherent and systematic overview of statistical methods in all stages of the process of analysing microarray data..." (Zentralblatt Math, Vol.1049, 2004)Table of ContentsPreface. 1 Preliminaries. 1.1 Using the R Computing Environment. 1.1.1 Installing smida. 1.1.2 Loading smida. 1.2 Data Sets from Biological Experiments. 1.2.1 Arabidopsis experiment: Anna Amtmann. 1.2.2 Skin cancer experiment: Nighean Barr. 1.2.3 Breast cancer experiment: John Bartlett. 1.2.4 Mammary gland experiment: Gusterson group. 1.2.5 Tuberculosis experiment: BµG@S group. I Getting Good Data. 2 Set-up of a Microarray Experiment. 2.1 Nucleic Acids: DNA and RNA. 2.2 Simple cDNA Spotted Microarray Experiment. 2.2.1 Growing experimental material. 2.2.2 Obtaining RNA. 2.2.3 Adding spiking RNA and poly-T primer. 2.2.4 Preparing the enzyme environment. 2.2.5 Obtaining labelled cDNA. 2.2.6 Preparing cDNA mixture for hybridization. 2.2.7 Slide hybridization. 3 Statistical Design of Microarrays. 3.1 Sources of Variation. 3.2 Replication. 3.2.1 Biological and technical replication. 3.2.2 How many replicates? 3.2.3 Pooling samples. 3.3 Design Principles. 3.3.1 Blocking, crossing and randomization. 3.3.2 Design and normalization. 3.4 Single-channelMicroarray Design. 3.4.1 Design issues. 3.4.2 Design layout. 3.4.3 Dealing with technical replicates. 3.5 Two-channelMicroarray Designs. 3.5.1 Optimal design of dual-channel arrays. 3.5.2 Several practical two-channel designs. 4 Normalization. 4.1 Image Analysis. 4.1.1 Filtering. 4.1.2 Gridding. 4.1.3 Segmentation. 4.1.4 Quantification. 4.2 Introduction to Normalization. 4.2.1 Scale of gene expression data. 4.2.2 Using control spots for normalization. 4.2.3 Missing data. 4.3 Normalization for Dual-channel Arrays. 4.3.1 Order for the normalizations. 4.3.2 Spatial correction. 4.3.3 Background correction. 4.3.4 Dye effect normalization. 4.3.5 Normalization within and across conditions. 4.4 Normalization of Single-channel Arrays. 4.4.1 Affymetrix data structure. 4.4.2 Normalization of Affymetrix data. 5 Quality Assessment. 5.1 Using MIAME in Quality Assessment. 5.1.1 Components of MIAME. 5.2 Comparing Multivariate Data. 5.2.1 Measurement scale. 5.2.2 Dissimilarity and distance measures. 5.2.3 Representing multivariate data. 5.3 Detecting Data Problems. 5.3.1 Clerical errors. 5.3.2 Normalization problems. 5.3.3 Hybridization problems. 5.3.4 Array mishandling. 5.4 Consequences of Quality Assessment Checks. 6 Microarray Myths: Data. 6.1 Design. 6.1.1 Single-versus dual-channel designs? 6.1.2 Dye-swap experiments. 6.2 Normalization. 6.2.1 Myth: ‘microarray data is Gaussian’. 6.2.2 Myth: ‘microarray data is not Gaussian’. 6.2.3 Confounding spatial and dye effect. 6.2.4 Myth: ‘non-negative background subtraction’. II Getting Good Answers. 7 Microarray Discoveries. 7.1 Discovering Sample Classes. 7.1.1 Why cluster samples? 7.1.2 Sample dissimilarity measures. 7.1.3 Clustering methods for samples. 7.2 Exploratory Supervised Learning. 7.2.1 Labelled dendrograms. 7.2.2 Labelled PAM-type clusterings. 7.3 Discovering Gene Clusters. 7.3.1 Similarity measures for expression profiles. 7.3.2 Gene clustering methods. 8 Differential Expression. 8.1 Introduction. 8.1.1 Classical versus Bayesian hypothesis testing. 8.1.2 Multiple testing ‘problem’. 8.2 Classical Hypothesis Testing. 8.2.1 What is a hypothesis test? 8.2.2 Hypothesis tests for two conditions. 8.2.3 Decision rules. 8.2.4 Results from skin cancer experiment. 8.3 Bayesian Hypothesis Testing. 8.3.1 A general testing procedure. 8.3.2 Bayesian t-test. 9 Predicting Outcomes with Gene Expression Profiles. 9.1 Introduction. 9.1.1 Probabilistic classification theory. 9.1.2 Modelling and predicting continuous variables. 9.2 Curse of Dimensionality: Gene Filtering. 9.2.1 Use only significantly expressed genes. 9.2.2 PCA and gene clustering. 9.2.3 Penalized methods. 9.2.4 Biological selection. 9.3 Predicting ClassMemberships. 9.3.1 Variance-bias trade-off in prediction. 9.3.2 Linear discriminant analysis. 9.3.3 k-nearest neighbour classification. 9.4 Predicting Continuous Responses. 9.4.1 Penalized regression: LASSO. 9.4.2 k-nearest neighbour regression. 10 Microarray Myths: Inference. 10.1 Differential Expression. 10.1.1 Myth: ‘Bonferroni is too conservative’. 10.1.2 FPR and collective multiple testing. 10.1.3 Misinterpreting FDR. 10.2 Prediction and Learning. 10.2.1 Cross-validation. Bibliography. Index.

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Book SynopsisSensitivity analysis should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. For a non-expert, choosing the method of analysis for their model is complex, and depends on a number of factors. This book guides the non-expert through their problem in order to enable them to choose and apply the most appropriate method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It is focussed on the use of SIMLAB a widely distributed freely-available sensitivity analysis software package developed by the authors for solving problems in sensitivity analysis of statistical models. Other key features: Provides an accessible overview of the current most widely used methods for sensitivity analysis. Opens with a detailed worked example to explain the motivation behind the book. Includes a range of examples to help illustrate the cTrade Review"...an interesting and informative book..." (Technometrics, May 2005) "...provides an accessible overview of the most widely used sensitivity analysis methods." (Zentralblatt Math, Vol.1049, 2004) "...well written..." (Statistical Methods in Medical Research, Vol 14 2005) Table of ContentsPREFACE. 1. A WORKED EXAMPLE. 1.1 A simple model. 1.2 Modulus version of the simple model. 1.3 Six-factor version of the simple model. 1.4 The simple model ‘by groups’. 1.5 The (less) simple correlated-input model. 1.6 Conclusions. 2. GLOBAL SENSITIVITY ANALYSIS FOR IMPORTANCE ASSESSMENT. 2.1 Examples at a glance. 2.2 What is sensitivity analysis? 2.3 Properties of an ideal sensitivity analysis method. 2.4 Defensible settings for sensitivity analysis. 2.5 Caveats. 3. TEST CASES. 3.1 The jumping man. Applying variance-based methods. 3.2 Handling the risk of a financial portfolio: the problem of hedging. Applying Monte Carlo filtering and variance-based methods. 3.3 A model of fish population dynamics. Applying the method of Morris. 3.4 The Level E model. Radionuclide migration in the geosphere. Applying variance-based methods and Monte Carlo filtering. 3.5 Two spheres. Applying variance based methods in estimation/calibration problems. 3.6 A chemical experiment. Applying variance based methods in estimation/calibration problems. 3.7 An analytical example. Applying the method of Morris. 4. THE SCREENING EXERCISE. 4.1 Introduction. 4.2 The method of Morris. 4.3 Implementing the method. 4.4 Putting the method to work: an analytical example. 4.5 Putting the method to work: sensitivity analysis of a fish population model. 4.6 Conclusions. 5. METHODS BASED ON DECOMPOSING THE VARIANCE OF THE OUTPUT. 5.1 The settings. 5.2 Factors Prioritisation Setting. 5.3 First-order effects and interactions. 5.4 Application of Si to Setting ‘Factors Prioritisation’. 5.5 More on variance decompositions. 5.6 Factors Fixing (FF) Setting. 5.7 Variance Cutting (VC) Setting. 5.8 Properties of the variance based methods. 5.9 How to compute the sensitivity indices: the case of orthogonal input. 5.9.1 A digression on the Fourier Amplitude Sensitivity Test (FAST). 5.10 How to compute the sensitivity indices: the case of non-orthogonal input. 5.11 Putting the method to work: the Level E model. 5.11.1 Case of orthogonal input factors. 5.11.2 Case of correlated input factors. 5.12 Putting the method to work: the bungee jumping model. 5.13 Caveats. 6. SENSITIVITY ANALYSIS IN DIAGNOSTIC MODELLING: MONTE CARLO FILTERING AND REGIONALISED SENSITIVITY ANALYSIS, BAYESIAN UNCERTAINTY ESTIMATION AND GLOBAL SENSITIVITY ANALYSIS. 6.1 Model calibration and Factors Mapping Setting. 6.2 Monte Carlo filtering and regionalised sensitivity analysis. 6.2.1 Caveats. 6.3 Putting MC filtering and RSA to work: the problem of hedging a financial portfolio. 6.4 Putting MC filtering and RSA to work: the Level E test case. 6.5 Bayesian uncertainty estimation and global sensitivity analysis. 6.5.1 Bayesian uncertainty estimation. 6.5.2 The GLUE case. 6.5.3 Using global sensitivity analysis in the Bayesian uncertainty estimation. 6.5.4 Implementation of the method. 6.6 Putting Bayesian analysis and global SA to work: two spheres. 6.7 Putting Bayesian analysis and global SA to work: a chemical experiment. 6.7.1 Bayesian uncertainty analysis (GLUE case). 6.7.2 Global sensitivity analysis. 6.7.3 Correlation analysis. 6.7.4 Further analysis by varying temperature in the data set: fewer interactions in the model. 6.8 Caveats. 7. HOW TO USE SIMLAB. 7.1 Introduction. 7.2 How to obtain and install SIMLAB. 7.3 SIMLAB main panel. 7.4 Sample generation. 7.4.1 FAST. 7.4.2 Fixed sampling. 7.4.3 Latin hypercube sampling (LHS). 7.4.4 The method of Morris. 7.4.5 Quasi-Random LpTau. 7.4.6 Random. 7.4.7 Replicated Latin Hypercube (r-LHS). 7.4.8 The method of Sobol’. 7.4.9 How to induce dependencies in the input factors. 7.5 How to execute models. 7.6 Sensitivity analysis. 8. FAMOUS QUOTES: SENSITIVITY ANALYSIS IN THE SCIENTIFIC DISCOURSE. REFERENCES. INDEX.

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Book SynopsisFinite mixture models are typically used where the population being studied is heterogeneous in composition. This work aims to offer an up-to-date account of the major issues involved with finite modelling. There is a practical emphasis on the applications of mixture models.Trade Review"This is an excellent book.... I enjoyed reading this book. I recommend it highly to both mathematical and applied statisticians." (Technometrics, February 2002) "This book will become popular to many researchers...the material covered is so wide that it will make this book a standard reference for the forthcoming years." (Zentralblatt MATH, Vol. 963, 2001/13) "the material covered is so wide that it will make this book a standard reference for the forthcoming years." (Zentralblatt MATH, Vol.963, No.13, 2001) "This book is excellent reading...should also serve as an excellent handbook on mixture modelling..." (Mathematical Reviews, 2002b) "...contains valuable information about mixtures for researchers..." (Journal of Mathematical Psychology, 2002) "...a masterly overview of the area...It is difficult to ask for more and there is no doubt that McLachlan and Peel's book will be the standard reference on mixture models for many years to come." (Statistical Methods in Medical Research, Vol. 11, 2002) "...they are to be congratulated on the extent of their achievement..." (The Statistician, Vol.51, No.3)Table of ContentsGeneral Introduction. ML Fitting of Mixture Models. Multivariate Normal Mixtures. Bayesian Approach to Mixture Analysis. Mixtures with Nonnormal Components. Assessing the Number of Components in Mixture Models. Multivariate t Mixtures. Mixtures of Factor Analyzers. Fitting Mixture Models to Binned Data. Mixture Models for Failure-Time Data. Mixture Analysis of Directional Data. Variants of the EM Algorithm for Large Databases. Hidden Markov Models. Appendices. References. Indexes.

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Book SynopsisThis book provides applied biologists and ecologists with the mathematical tools they need to understand the ever increasingly mathematical and complex area of population ecology.Table of ContentsSampling in Applied Population Ecology. The Role of Abiotic Factors. Life Tables. Resource Acquisition in Predator-Prey Systems. Resource Acquisition and Allocation. MODELING: A PREVIEW. Simple Single-Species Models. Simple Models of Multitropic Interactions. Single-Species Models with Age Structure. Realistic Age-Structured Multitrophic Models. Regional Dynamics. Ecosystem Sustainability. Appendices. References. Indexes.

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Book SynopsisOceanography calls for a wide variety of mathematical and statistical techniques. This title provides the basics oceanographers need to know, including: practical ways to deal with chemical, geological, and biological oceanographic data; and instructions on detecting the existence of patterns in what appears to be noise.Trade Review"...It presents many well discussed and illustrative examples..." (Zentralblatt Math, Vol.988, No.13, 2002)Table of ContentsCalculus Review. Model I Linear Regression. Correlation. Model II Linear Regression. Polynomial Curve Fitting, Linear Multiple Regression Analysis, andNonlinear Least Squares. Numerical Integration. Box Models. Time Series Analysis. Appendices. Answers to Exercises. Index.

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