Stochastics Books
Springer Verlag, Singapore Introduction to Stochastic Calculus
This book sheds new light on stochastic calculus, the branch of mathematics that is most widely applied in financial engineering and mathematical finance. The first book to introduce pathwise formulae for the stochastic integral, it provides a simple but rigorous treatment of the subject, including a range of advanced topics. The book discusses in-depth topics such as quadratic variation, Ito formula, and Emery topology. The authors briefly addresses continuous semi-martingales to obtain growth estimates and study solution of a stochastic differential equation (SDE) by using the technique of random time change. Later, by using Metivier–Pellaumail inequality, the solutions to SDEs driven by general semi-martingales are discussed. The connection of the theory with mathematical finance is briefly discussed and the book has extensive treatment on the representation of martingales as stochastic integrals and a second fundamental theorem of asset pricing. Intended for undergraduate- and beginning graduate-level students in the engineering and mathematics disciplines, the book is also an excellent reference resource for applied mathematicians and statisticians looking for a review of the topic.
£85.49
Springer Nature Switzerland AG Probability with Statistical Applications
Book SynopsisThis second edition textbook offers a practical introduction to probability for undergraduates at all levels with different backgrounds and views towards applications. Calculus is a prerequisite for understanding the basic concepts, however the book is written with a sensitivity to students’ common difficulties with calculus that does not obscure the thorough treatment of the probability content. The first six chapters of this text neatly and concisely cover the material traditionally required by most undergraduate programs for a first course in probability. The comprehensive text includes a multitude of new examples and exercises, and careful revisions throughout. Particular attention is given to the expansion of the last three chapters of the book with the addition of one entirely new chapter (9) on ’Finding and Comparing Estimators.’ The classroom-tested material presented in this second edition forms the basis for a second course introducing mathematical statistics.Table of ContentsProbability Space.- Conditional probabilities.- Discrete random variables.- Binomial random variables.- Poisson random variables.- Simulations of discrete random variables.- Combinatorics.- Continuous random variables.- The sample average and sample.- Estimating and testing proportions.- Estimating and testing means.- Small samples.- Chi-squared tests.- Design of experiments.- The cumulative distribution function.- Continuous joint distributions.- Covariance and independence.- Conditional distribution and expectation.- The bivariate normal distribution.- Sums of Bernoulli random variables.- Coupling random variables.- The moment generating function.- The chi-squared, Student and F distributions.- Sampling from a normal distribution.- Finding estimators.- Comparing estimators.- Best unbiased estimators.- Bayes’ estimator.- Multiple linear regression.- List of common discrete distributions.- List of common continuous distributions.- Further reading.- Normal table.- Student table.- Chi-squared table.- Index.
£44.99
Princeton University Press Stochastic Thermodynamics
Book Synopsis
£63.00
CRC Press Multiple Stopping Problems
Book SynopsisThis book presents the theory of rational decisions involving the selection of stopping times in observed discrete-time stochastic processes, both by single and multiple decision-makers. Readers will become acquainted with the models, strategies, and applications of these models.It begins with an examination of selected models framed as stochastic optimization challenges, emphasizing the critical role of optimal stopping times in sequential statistical procedures. The authors go on to explore models featuring multiple stopping and shares on leading applications, particularly focusing on change point detection, selection problems, and the nuances of behavioral ecology. In the following chapters, an array of perspectives on model strategies is presented, elucidating their interpretation and the methodologies underpinning their genesis. Essential notations and definitions are introduced, examining general theorems about solution existence and structure, with an intricate analysi
£44.99
Springer London Ltd A Modern Introduction to Probability and
Book SynopsisSuitable for self study Use real examples and real data sets that will be familiar to the audience Introduction to the bootstrap is included – this is a modern method missing in many other books Trade ReviewFrom the reviews: "[the material is] superbly motivated with interest-grabbing examples... exercises excellent and plentiful." Edward Williams, University of Michigan-Dearborn, USA "... it is a notoriously hard task to introduce probability and statistics with a mix of intuition and mathematics to keep students motivated. Therefore, I very much welcome this book and recommend it as course material." Sara van de Geer, Leiden University, The Netherlands "This textbook provides a well-written first course in probability and statistics...It is a book that has been written based on the long teaching experience of the authors and I would certainly recommend it for university coursework." Short Book Reviews of the International Statistical Institute, December 2005 "This book has numerous quick exercises to give direct feedback to the students. … A website at www.springeronline.com/978-1-85233-896-1 gives access to the data files used in the text … . This will be a key text for undergraduates in computer science, physics, mathematics, chemistry, biology and business studies who are studying a mathematical statistics course, and also for more intensive engineering statistics courses for undergraduates in all engineering subjects." (Rainer Beedgen, Zentralblatt MATH, Vol. 1079, 2006) "The book is designed for a one-semester introductory course in probability and statistics basics for engineering students. … It can also be used by students in other more mathematically oriented majors such as applied mathematics with more emphasis on the mathematics and additional coverage in topics such as combinatorics, conditional expectation, and generating functions. … More elaborate exercises and real datasets are given at the end of each chapter." (Arthur B. Yeh, Technometrics, Vol. 49 (3), August, 2007)Table of ContentsWhy probability and statistics?.- Outcomes, events, and probability.- Conditional probability and independence.- Discrete random variables.- Continuous random variables.- Simulation.- Expectation and variance.- Computations with random variables.- Joint distributions and independence.- Covariance and correlation.- More computations with more random variables.- The Poisson process.- The law of large numbers.- The central limit theorem.- Exploratory data analysis: graphical summaries.- Exploratory data analysis: numerical summaries.- Basic statistical models.- The bootstrap.- Unbiased estimators.- Efficiency and mean squared error.- Maximum likelihood.- The method of least squares.- Confidence intervals for the mean.- More on confidence intervals.- Testing hypotheses: essentials.- Testing hypotheses: elaboration.- The t-test.- Comparing two samples.
£29.69
Springer London Ltd A Modern Introduction to Probability and
Book SynopsisSuitable for self study Use real examples and real data sets that will be familiar to the audience Introduction to the bootstrap is included – this is a modern method missing in many other books Trade ReviewFrom the reviews: "[the material is] superbly motivated with interest-grabbing examples... exercises excellent and plentiful." Edward Williams, University of Michigan-Dearborn, USA "... it is a notoriously hard task to introduce probability and statistics with a mix of intuition and mathematics to keep students motivated. Therefore, I very much welcome this book and recommend it as course material." Sara van de Geer, Leiden University, The Netherlands "This textbook provides a well-written first course in probability and statistics...It is a book that has been written based on the long teaching experience of the authors and I would certainly recommend it for university coursework." Short Book Reviews of the International Statistical Institute, December 2005 "This book has numerous quick exercises to give direct feedback to the students. … A website at www.springeronline.com/978-1-85233-896-1 gives access to the data files used in the text … . This will be a key text for undergraduates in computer science, physics, mathematics, chemistry, biology and business studies who are studying a mathematical statistics course, and also for more intensive engineering statistics courses for undergraduates in all engineering subjects." (Rainer Beedgen, Zentralblatt MATH, Vol. 1079, 2006) "The book is designed for a one-semester introductory course in probability and statistics basics for engineering students. … It can also be used by students in other more mathematically oriented majors such as applied mathematics with more emphasis on the mathematics and additional coverage in topics such as combinatorics, conditional expectation, and generating functions. … More elaborate exercises and real datasets are given at the end of each chapter." (Arthur B. Yeh, Technometrics, Vol. 49 (3), August, 2007)Table of ContentsWhy probability and statistics?.- Outcomes, events, and probability.- Conditional probability and independence.- Discrete random variables.- Continuous random variables.- Simulation.- Expectation and variance.- Computations with random variables.- Joint distributions and independence.- Covariance and correlation.- More computations with more random variables.- The Poisson process.- The law of large numbers.- The central limit theorem.- Exploratory data analysis: graphical summaries.- Exploratory data analysis: numerical summaries.- Basic statistical models.- The bootstrap.- Unbiased estimators.- Efficiency and mean squared error.- Maximum likelihood.- The method of least squares.- Confidence intervals for the mean.- More on confidence intervals.- Testing hypotheses: essentials.- Testing hypotheses: elaboration.- The t-test.- Comparing two samples.
£26.59
Oxford University Press Probability
Book SynopsisProbability is an area of mathematics of tremendous contemporary importance across all aspects of human endeavour. This book is a compact account of the basic features of probability and random processes at the level of first and second year mathematics undergraduates and Masters'' students in cognate fields. It is suitable for a first course in probability, plus a follow-up course in random processes including Markov chains.A special feature is the authors'' attention to rigorous mathematics: not everything is rigorous, but the need for rigour is explained at difficult junctures. The text is enriched by simple exercises, together with problems (with very brief hints) many of which are taken from final examinations at Cambridge and Oxford. The first eight chapters form a course in basic probability, being an account of events, random variables, and distributions - discrete and continuous random variables are treated separately - together with simple versions of the law of large numbersTable of ContentsPART A BASIC PROBABILITY; PART B FURTHER PROBABILITY
£38.99
Oxford University Press Probability and Random Processes
Book SynopsisThe fourth edition of this successful text provides an introduction to probability and random processes, with many practical applications. It is aimed at mathematics undergraduates and postgraduates, and has four main aims.US BL To provide a thorough but straightforward account of basic probability theory, giving the reader a natural feel for the subject unburdened by oppressive technicalities. BE BL To discuss important random processes in depth with many examples.BE BL To cover a range of topics that are significant and interesting but less routine. BE BL To impart to the beginner some flavour of advanced work.BE UE OP The book begins with the basic ideas common to most undergraduate courses in mathematics, statistics, and science. It ends with material usually found at graduate level, for example, Markov processes, (including Markov chain Monte Carlo), martingales, queues, diffusions, (including stochastic calculus with Itô''s formula), renewals, stationary processes (including the ergodic theorem), and option pricing in mathematical finance using the Black-Scholes formula. Further, in this new revised fourth edition, there are sections on coupling from the past, Lévy processes, self-similarity and stability, time changes, and the holding-time/jump-chain construction of continuous-time Markov chains. Finally, the number of exercises and problems has been increased by around 300 to a total of about 1300, and many of the existing exercises have been refreshed by additional parts. The solutions to these exercises and problems can be found in the companion volume, One Thousand Exercises in Probability, third edition, (OUP 2020).CPTrade ReviewFeatures of PRP include brief but helpful motivational introductions to each subsection, and copious references to historical applications. To aid navigation, definitions, theorems and other key results are highlighted, using three different colours. The tone throughout is rigorous but the touch is human ... * Owen Toller, The Mathematical Gazette *Since its first appearance in 1982 Probability and Random Processes has been a landmark book on the subject and has become mandatory reading for any mathematician wishing to understand chance. It is aimed mainly at final-year honours students and graduate students, but it goes beyond this level, and all serious mathematicians and academic libraries should own a copy ... the companion book of exercises is cleverly conceived and ... forms a perfect complement to the main text. * Times Higher Education Supplement *Review from previous edition...a full and comprehensive account of (almost all) the probability theory and stochastic processes one could hope to teach to undergraduates.... As well as its masterful coverage of the material, the book has many appealing stylistic features ... extremely valuable in finding good proofs of theorems which are dealt with rather cursorily in other textbooks. * The Mathematical Gazette *One of the strong features of the book is its large collection of interesting exercises, which has been greatly expanded in this new edition so that there are now over one thousand. These are conveniently collected together in a separate volume that includes full solutions. * Biometrics *Table of Contents1: Events and their probabilities 2: Random variables and their distributions 3: Discrete random variables 4: Continuous random variables 5: Generating functions and their applications 6: Markov chains 7: Convergence of random variables 8: Random processes 9: Stationary processes 10: Renewals 11: Queues 12: Martingales 13: Diffusion processes
£50.35
Oxford University Press One Thousand Exercises in Probability
Book SynopsisThis third edition is a revised, updated, and greatly expanded version of previous edition of 2001. The 1300+ exercises contained within are not merely drill problems, but have been chosen to illustrate the concepts, illuminate the subject, and both inform and entertain the reader. A broad range of subjects is covered, including elementary aspects of probability and random variables, sampling, generating functions, Markov chains, convergence, stationary processes, renewals, queues, martingales, diffusions, Lévy processes, stability and self-similarity, time changes, and stochastic calculus including option pricing via the Black-Scholes model of mathematical finance.The text is intended to serve students as a companion for elementary, intermediate, and advanced courses in probability, random processes and operations research. It will also be useful for anyone needing a source for large numbers of problems and questions in these fields. In particular, this book acts as a companion to the authors'' volume, Probability and Random Processes, fourth edition (OUP 2020).Trade ReviewReview from previous edition "A useful source of exercises and problems from probability theory and random process. I have read it with great pleasure and can recommend it to students and teachers." * EMS *Table of Contents1: Events and their probabilities 2: Random variables and their distributions 3: Discrete random variables 4: Continuous random variables 5: Generating functions and their applications 6: Markov chains 7: Convergence of random variables 8: Random processes 9: Stationary processes 10: Renewals 11: Queues 12: Martingales 13: Diffusion processes
£39.49
Oxford University Press Arbitrage Theory in Continuous Time
Book SynopsisThe fourth edition of this widely used textbook on pricing and hedging of financial derivatives now also includes dynamic equilibrium theory and continues to combine sound mathematical principles with economic applications. Concentrating on the probabilistic theory of continuous time arbitrage pricing of financial derivatives, including stochastic optimal control theory and optimal stopping theory, Arbitrage Theory in Continuous Time is designed for graduate students in economics and mathematics, and combines the necessary mathematical background with a solid economic focus. It includes a solved example for every new technique presented, contains numerous exercises, and suggests further reading in each chapter. All concepts and ideas are discussed, not only from a mathematics point of view, but with lots of intuitive economic arguments.In the substantially extended fourth edition Tomas Björk has added completely new chapters on incomplete markets, treating such topics as the Esscher trTrade ReviewReview from previous edition This book is one of the best of a large number of new books on mathematical and probabilistic models in finance, positioned between the books by Hull and Duffie on a mathematical scale...This is a highly reasonable book and strikes a balance between mathematical development and intuitive explanation. * Short Book Reviews *Table of Contents1: Introduction I. Discrete Time Models 2: The Binomial Model 3: A More General One period Model II. Stochastic Calculus 4: Stochastic Integrals 5: Stochastic Differential Equations III. Arbitrage Theory 6: Portfolio Dynamics 7: Arbitrage Pricing 8: Completeness and Hedging 9: A Primer on Incomplete Markets 10: Parity Relations and Delta Hedging 11: The Martingale Approach to Arbitrage Theory 12: The Mathematics of the Martingale Approach 13: Black-Scholes from a Martingale Point of View 14: Multidimensional Models: Martingale Approach 15: Change of Numeraire 16: Dividends 17: Forward and Futures Contracts 18: Currency Derivatives 19: Bonds and Interest Rates 20: Short Rate Models 21: Martingale Models for the Short Rate 22: Forward Rate Models 23: LIBOR Market Models 24: Potentials and Positive Interest IV. Optimal Control and Investment Theory 25: Stochastic Optimal Control 26: Optimal Consumption and Investment 27: The Martingale Approach to Optimal Investment 28: Optimal Stopping Theory and American Options V. Incomplete Markets 29: Incomplete Markets 30: The Esscher Transform and the Minimal Martingale Measure 31: Minimizing f-divergence 32: Portfolio Optimization in Incomplete Markets 33: Utility Indifference Pricing and Other Topics 34: Good Deal Bounds VI. Dynamic Equilibrium Theory 35: Equilibrium Theory: A Simple Production Model 36: The Cox-Ingersoll-Ross Factor Model 37: The Cox-Ingersoll-Ross Interest Rate Model 38: Endowment Equilibrium: Unit Net Supply
£59.85
Introduction to Probability Models
Book SynopsisTable of Contents1. Introduction to Probability Theory 2. Random Variables 3. Conditional Probability and Conditional Expectation 4. Markov Chains 5. The Exponential Distribution and the Poisson Process 6. Continuous-Time Markov Chains 7. Renewal Theory and Its Applications 8. Queueing Theory 9. Reliability Theory 10. Brownian Motion and Stationary Processes 11. Simulation 12. Coupling 13. Martingales
£86.40
Springer Nature Switzerland AG Probability Theory: A Comprehensive Course
Book SynopsisThis popular textbook, now in a revised and expanded third edition, presents a comprehensive course in modern probability theory.Probability plays an increasingly important role not only in mathematics, but also in physics, biology, finance and computer science, helping to understand phenomena such as magnetism, genetic diversity and market volatility, and also to construct efficient algorithms. Starting with the very basics, this textbook covers a wide variety of topics in probability, including many not usually found in introductory books, such as: limit theorems for sums of random variables martingales percolation Markov chains and electrical networks construction of stochastic processes Poisson point process and infinite divisibility large deviation principles and statistical physics Brownian motion stochastic integrals and stochastic differential equations. The presentation is self-contained and mathematically rigorous, with the material on probability theory interspersed with chapters on measure theory to better illustrate the power of abstract concepts.This third edition has been carefully extended and includes new features, such as concise summaries at the end of each section and additional questions to encourage self-reflection, as well as updates to the figures and computer simulations. With a wealth of examples and more than 290 exercises, as well as biographical details of key mathematicians, it will be of use to students and researchers in mathematics, statistics, physics, computer science, economics and biology.Table of Contents1 Basic Measure Theory.- 2 Independence.- 3 Generating Functions.- 4 The Integral.- 5 Moments and Laws of Large Numbers.- 6 Convergence Theorems.- 7 Lp-Spaces and the Radon–Nikodym Theorem.- 8 Conditional Expectations.- 9 Martingales.- 10 Optional Sampling Theorems.- 11 Martingale Convergence Theorems and Their Applications.- 12 Backwards Martingales and Exchangeability.- 13 Convergence of Measures.- 14 Probability Measures on Product Spaces.- 15 Characteristic Functions and the Central Limit Theorem.- 16 Infinitely Divisible Distributions.- 17 Markov Chains.- 18 Convergence of Markov Chains.- 19 Markov Chains and Electrical Networks.- 20 Ergodic Theory.- 21 Brownian Motion.- 22 Law of the Iterated Logarithm.- 23 Large Deviations.- 24 The Poisson Point Process.- 25 The Itô Integral.- 26 Stochastic Differential Equations.- References.- Notation Index.- Name Index.- Subject Index.
£52.24
Springer Nature Switzerland AG Foundations and Methods of Stochastic Simulation:
Book SynopsisThis graduate-level textbook covers modelling, programming and analysis of stochastic computer simulation experiments, including the mathematical and statistical foundations of simulation and why it works. The book is rigorous and complete, but concise and accessible, providing all necessary background material. Object-oriented programming of simulations is illustrated in Python, while the majority of the book is programming language independent. In addition to covering the foundations of simulation and simulation programming for applications, the text prepares readers to use simulation in their research. A solutions manual for end-of-chapter exercises is available for instructors.Table of ContentsChapter 1: Why Do We Simulate.- Chapter 2: Simulation Programming: Quick Start.- Chapter 3: Examples.- Chapter 4: Simulation Programming with PythonSim.- Chapter 5: Three Views of Simulation.- Chapter 6: Simulation Input.- Chapter 7: Simulation Output.- Chapter 8: Experiment Design and Analysis.- Chapter 9: Simulation Optimization and Sensitivity.- Chapter 10: Simulation for Research.- References.- Index.
£47.99
Springer International Publishing AG Brownian Motion, Martingales, and Stochastic
Book SynopsisThis book offers a rigorous and self-contained presentation of stochastic integration and stochastic calculus within the general framework of continuous semimartingales. The main tools of stochastic calculus, including Itô’s formula, the optional stopping theorem and Girsanov’s theorem, are treated in detail alongside many illustrative examples. The book also contains an introduction to Markov processes, with applications to solutions of stochastic differential equations and to connections between Brownian motion and partial differential equations. The theory of local times of semimartingales is discussed in the last chapter.Since its invention by Itô, stochastic calculus has proven to be one of the most important techniques of modern probability theory, and has been used in the most recent theoretical advances as well as in applications to other fields such as mathematical finance. Brownian Motion, Martingales, and Stochastic Calculus provides a strong theoretical background to the reader interested in such developments.Beginning graduate or advanced undergraduate students will benefit from this detailed approach to an essential area of probability theory. The emphasis is on concise and efficient presentation, without any concession to mathematical rigor. The material has been taught by the author for several years in graduate courses at two of the most prestigious French universities. The fact that proofs are given with full details makes the book particularly suitable for self-study. The numerous exercises help the reader to get acquainted with the tools of stochastic calculus.Trade Review“‘The aim of this book is to provide a rigorous introduction to the theory of stochastic calculus for continuous semi-martingales putting a special emphasis on Brownian motion.’ … If the reader has the background and needs a rigorous treatment of the subject this book would be a good choice. Le Gall writes clearly and gets to the point quickly … .” (Richard Durrett, MAA Reviews, March, 2017) “The purpose of this book is to provide concise but rigorous introduction to the theory of stochastic calculus for continuous semimartingales, putting a special emphasis on Brownian motion. … The book is written very clearly, it is interesting both for its construction and maintenance, mostly it is self-contained. It can be recommended to everybody who wants to study stochastic calculus, including those who is interested to its applications in other fields.” (Yuliya S. Mishura, zbMATH, 2017)Table of ContentsGaussian variables and Gaussian processes.- Brownian motion.- Filtrations and martingales.- Continuous semimartingales.- Stochastic integration.- General theory of Markov processes.- Brownian motion and partial differential equations.- Stochastic differential equations.- Local times.- The monotone class lemma.- Discrete martingales.- References.
£38.69
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Stochastic Differential Equations: An
Book SynopsisThis edition contains detailed solutions of selected exercises. Many readers have requested this, because it makes the book more suitable for self-study. At the same time new exercises (without solutions) have beed added. They have all been placed in the end of each chapter, in order to facilitate the use of this edition together with previous ones. Several errors have been corrected and formulations have been improved. This has been made possible by the valuable comments from (in alphabetical order) Jon Bohlin, Mark Davis, Helge Holden, Patrick Jaillet, Chen Jing, Natalia Koroleva,MarioLefebvre,Alexander Matasov,Thilo Meyer-Brandis, Keigo Osawa, Bjorn Thunestvedt, Jan Uboe and Yngve Williassen. I thank them all for helping to improve the book. My thanks also go to Dina Haraldsson, who once again has performed the typing and drawn the ?gures with great skill. Blindern, September 2002 Bernt Oksendal xv Preface to Corrected Printing, Fifth Edition The main corrections and improvements in this corrected printing are from Chapter 12. I have bene?tted from useful comments from a number of p- ple, including (in alphabetical order) Fredrik Dahl, Simone Deparis, Ulrich Haussmann, Yaozhong Hu, Marianne Huebner, Carl Peter Kirkebo, Ni- lay Kolev, Takashi Kumagai, Shlomo Levental, Geir Magnussen, Anders Oksendal, Jur . . gen Pottho?, Colin Rowat, Stig Sandnes, Lones Smith, S- suo Taniguchi and Bjorn Thunestvedt. I want to thank them all for helping me making the book better. I also want to thank Dina Haraldsson for pro?cient typing.Trade ReviewFrom the reviews of the fifth edition: "This is a highly readable and refreshingly rigorous introduction to stochastic calculus. … This is not a watered-down treatment. It is a serious introduction that starts with fundamental measure-theoretic concepts and ends, coincidentally, with the Black-Scholes formula as one of several examples of applications. This is the best single resource for learning the stochastic calculus … ." (riskbook.com, 2002) From the reviews of the sixth edition: "The book … has evolved from a 200-page typewritten booklet to a modern classic. Part of its charm and success is the fact that the author does not bother too much with the (for the novice) cumbersome rigorous theory … . This does not mean that the book is not rigorous, it is just the timing and dosage of mathematical rigour … that is palatable for undergraduates … . a highly readable account, suitable for self-study and for use in the classroom." (René L. Schilling, The Mathematical Gazette, March, 2005) "This is the sixth edition of the classical and excellent book on stochastic differential equations. The main difference with the next to last edition is the addition of detailed solutions of selected exercises … . This is certainly an excellent idea in view to test its ability of applications of the concepts … . certainly one of the best books on the subject, it will be very helpful to any graduate students and also very valuable for any analysts of financial market." (Stéphane Métens, Physicalia, Vol. 26 (1), 2004) "This is now the sixth edition of the excellent book on stochastic differential equations and related topics. … the presentation is successfully balanced between being easily accessible for a broad audience and being mathematically rigorous. The book is a first choice for courses at graduate level in applied stochastic differential equations. The inclusion of detailed solutions to many of the exercises in this edition also makes it very useful for self-study." (Evelyn Buckwar, Zentralblatt MATH, Vol. 1025, 2003)Table of ContentsSome Mathematical Preliminaries.- Itô Integrals.- The Itô Formula and the Martingale Representation Theorem.- Stochastic Differential Equations.- The Filtering Problem.- Diffusions: Basic Properties.- Other Topics in Diffusion Theory.- Applications to Boundary Value Problems.- Application to Optimal Stopping.- Application to Stochastic Control.- Application to Mathematical Finance.
£47.49
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Basic Stochastic Processes: A Course Through
Book SynopsisStochastic processes are tools used widely by statisticians and researchers working in the mathematics of finance. This book for self-study provides a detailed treatment of conditional expectation and probability, a topic that in principle belongs to probability theory, but is essential as a tool for stochastic processes. The book centers on exercises as the main means of explanation.Trade ReviewThis book fulfils its aim of providing good and interesting material for advanced undergraduate study. The Times Higher Education Supplement This is probably one of the best books to begin learning about the sometimes complex topic of stochastic calculus and stochastic processes from a more mathematical approach. Some literature are often accused of unnecessarily complicating the subject when applied to areas of finance. With this book you are allowed to explore the rigorous side of stochastic calculus, yet maintain a physical insight of what is going on. The authors have concentrated on the most important and useful topics that are encountered in common physical and financial systems www.quantnotes.com Table of Contents1. Review of Probability.- 1.1 Events and Probability.- 1.2 Random Variables.- 1.3 Conditional Probability and Independence.- 1.4 Solutions.- 2. Conditional Expectation.- 2.1 Conditioning on an Event.- 2.2 Conditioning on a Discrete Random Variable.- 2.3 Conditioning on an Arbitrary Random Variable.- 2.4 Conditioning on a ?-Field.- 2.5 General Properties.- 2.6 Various Exercises on Conditional Expectation.- 2.7 Solutions.- 3. Martingales in Discrete.- 3.1 Sequences of Random Variables.- 3.2 Filtrations.- 3.3 Martingales.- 3.4 Games of Chance.- 3.5 Stopping Times.- 3.6 Optional Stopping Theorem.- 3.7 Solutions.- 4. Martingale Inequalities and Convergence.- 4.1 Doob’s Martingale Inequalities.- 4.2 Doob’s Martingale Convergence Theorem.- 4.3 Uniform Integrability and L1 Convergence of Martingales.- 4.4 Solutions.- 5. Markov Chains.- 5.1 First Examples and Definitions.- 5.2 Classification of States.- 5.3 Long-Time Behaviour of Markov Chains: General Case.- 5.4 Long-Time Behaviour of Markov Chains with Finite State Space.- 5.5 Solutions.- 6. Stochastic Processes in Continuous Time.- 6.1 General Notions.- 6.2 Poisson Process.- 6.2.1 Exponential Distribution and Lack of Memory.- 6.2.2 Construction of the Poisson Process.- 6.2.3 Poisson Process Starts from Scratch at Time t.- 6.2.4 Various Exercises on the Poisson Process.- 6.3 Brownian Motion.- 6.3.1 Definition and Basic Properties.- 6.3.2 Increments of Brownian Motion.- 6.3.3 Sample Paths.- 6.3.4 Doob’s Maximal L2 Inequality for Brownian Motion.- 6.3.5 Various Exercises on Brownian Motion.- 6.4 Solutions.- 7. Itô Stochastic Calculus.- 7.1 Itô Stochastic Integral: Definition.- 7.2 Examples.- 7.3 Properties of the Stochastic Integral.- 7.4 Stochastic Differential and Itô Formula.- 7.5 Stochastic Differential Equations.- 7.6 Solutions.
£28.49
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Stochastic Processes and Financial Mathematics
Book SynopsisThe book provides an introduction to advanced topics in stochastic processes and related stochastic analysis, and combines them with a sound presentation of the fundamentals of financial mathematics. It is wide-ranging in content, while at the same time placing much emphasis on good readability, motivation, and explanation of the issues covered. Financial mathematical topics are first introduced in the context of discrete time processes and then transferred to continuous-time models. The basic construction of the stochastic integral and the associated martingale theory provide fundamental methods of the theory of stochastic processes for the construction of suitable stochastic models of financial mathematics, e.g. using stochastic differential equations. Central results of stochastic analysis such as the Itô formula, Girsanov's theorem and martingale representation theorems are of fundamental importance in financial mathematics, e.g. for the risk-neutral valuation formula (Black-Scholes formula) or the question of the hedgeability of options and the completeness of market models. Chapters on the valuation of options in complete and incomplete markets and on the determination of optimal hedging strategies conclude the range of topics.Advanced knowledge of probability theory is assumed, in particular of discrete-time processes (martingales, Markov chains) and continuous-time processes (Brownian motion, Lévy processes, processes with independent increments, Markov processes). The book is thus suitable for advanced students as a companion reading and for instructors as a basis for their own courses.This book is a translation of the original German 1st edition Stochastische Prozesse und Finanzmathematik by Ludger Rüschendorf, published by Springer-Verlag GmbH Germany, part of Springer Nature in 2020. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com) and in a subsequent editing, improved by the author. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.Table of ContentsOption pricing in models in discrete time.- Scorohod's embedding theorem and Donsker's theorem.- Stochastic integration.- Elements of stochastic analysis.- Option pricing in complete and incomplete markets.- Utility optimization, minimum distance martingales, and utility indiff.- Variance-minimum hedging.
£49.49
Elsevier Science Publishing Co Inc Introduction to Probability and Statistics for
Book SynopsisTable of ContentsCHAPTER 1 Introduction to statistics CHAPTER 2 Descriptive statistics CHAPTER 3 Elements of probability CHAPTER 4 Random variables and expectation CHAPTER 5 Special random variables CHAPTER 6 Distributions of sampling statistics CHAPTER 7 Parameter estimation CHAPTER 8 Hypothesis testing CHAPTER 9 Regression CHAPTER 10 Analysis of variance CHAPTER 11 Goodness of fit tests and categorical data analysis CHAPTER 12 Nonparametric hypothesis tests CHAPTER 13 Quality control CHAPTER 14 Life testing CHAPTER 15 Simulation, bootstrap statistical methods, and permutation tests CHAPTER 16 Machine learning and big data
£93.09
CRC Press Bayesian Analysis of Time Series
Book SynopsisIn many branches of science relevant observations are taken sequentially over time. Bayesian Analysis of Time Series discusses how to use models that explain the probabilistic characteristics of these time series and then utilizes the Bayesian approach to make inferences about their parameters. This is done by taking the prior information and via Bayes theorem implementing Bayesian inferences of estimation, testing hypotheses, and prediction. The methods are demonstrated using both R and WinBUGS. The R package is primarily used to generate observations from a given time series model, while the WinBUGS packages allows one to perform a posterior analysis that provides a way to determine the characteristic of the posterior distribution of the unknown parameters.Features Presents a comprehensive introduction to the Bayesian analysis of time series. Gives many examples over a wide variety of fieldsTrade Review"...(This book) by Lyle D. Broemeling is an excellent source to learn time series concepts, methods, expressions, and interpretations from the Bayesian viewpoint using R code and WinBugs code...The book is suitable for usage to teach in a graduate-level Bayesian time series course...The references are exhaustive and well selected for the readers. The exercises are challenging."- Ramalingam Shanmugam, JSCS, Aug 2020 Table of Contents1. Introduction. 2. Bayesian Inference : The prior, posterior and predictive distributions. 3. Plot Trends , Seasonal Variation and Decomposition of a Series. 4. Autocorrelation, Partial Correlation, and Cross Correlation. 5. Bayesian Data Analysis for Some Fundamental Time Series. 6. Bayesian Regression Analysis with Time Series Errors. 7. Bayesian Methods for Stationary Models 8. An Analysis for Non-Stationary Models. 9. Bayesian Spectrum Analysis. 10. System Identification from a Bayesian Perspective. 11. Multivariate Models. 12. Dynamic Linear Models for Time Series. 13. Bayesian Posterior Distributions for Non-Linear Models.14. Bilinear Models and Threshold Autoregressive Processes. 15. Miscellaneous Topics in Time Series.
£47.99
Springer-Verlag New York Inc. Survival and Event History Analysis A Process
Book SynopsisThe aim of this book is to bridge the gap between standard textbook models and a range of models where the dynamic structure of the data manifests itself fully. they allow sensible interpretations of a number of surprising artifacts seen in population data.The stochastic process framework is naturally connected to causality.Trade ReviewFrom the reviews: "The book is intended as a text for biostatistics graduate students. It will fill that role excellently. It will expose them to ideas they are unlikely to encounter in depth in a standard curriculum and is precisely the sort of book to inspire theses and other research projects. Prerequisites include exposure to stochastic processes and basic survival analysis, as well as the mathematical statistics that the standard graduate program provides. Each chapter contains relevant probability theory and data analyses and concludes with a set of exercises.... With its comprehensive and up-to-date bibliography, and extensive index, it is also ideal for self-study.… The book has Springer’s high quality with pleasing typesetting and good margins." (Patricia Grambsch, Biometrics, June 2009, 65) "…typifies the authors’ interest in understanding the mechanisms that underlie developments over times, as does the brilliant chapter on causality. In summary, Aalen, Borgan, and Gjessing have managed to write a book which is both practical and thought-provoking, wide-ranging yet focused, and above all, accessible. It will be around for a long time." (Robin Henderson, Significance, September 2009) "Readership: Practicing statisticians as well as theoreticians interested in survival analysis. Also suitable for a graduate course. Very well written. … Aalen, Borgan and Gjessing have written a new book which is also likely to have a profound influence on the subject, possibly both from the classical and Bayesian point of view … . The book is based on point processes … . Deep facts about these processes as well as martingales and stochastic integrals are introduced and used throughout with clarity and intuitive insight." (Jayanta K. Ghosh, International Statistical Review, Vol. 77 (3), 2009) “This book intends to distinguish itself by presenting a broad and comprehensive view of stochastic processes which are useful for the analysis of survival data and, more generally, of ‘event histories’, i.e. series of occurrences of events over time. … In conclusion, this is an excellent book which will be useful to researchers in several fields due to the broad interest of the presented methodologies … .” (Bruno Betrò, Mathematical Reviews, Issue 2010 b) “Inspired by the spread of survival and event history analysis to fields beyond biostatistics and by the increasing complexity of high-quality data structures, the authors have written an elegant text that bridges theory and applications and balances technical detail with pedagogical simplicity. The book moves beyond other textbooks on the topic of survival and event history analysis by using a stochastic processes framework to develop models for events repeated over time or related among individuals. …Overall, the book is masterfully written and a welcome addition to the bookshelf of anyone doing either applied modeling or methodological research in survival or event history analysis.” (Journal of the American Statistical Association, Vol. 105, No. 489)Table of ContentsAn introduction to survival and event history analysis.- Stochastic processes in event history analysis.- Nonparametric analysis of survival and event history data.- Regression models.- Parametric counting process models.- Unobserved heterogeneity: The odd effects of frailty.- Multivariate frailty models.- Marginal and dynamic models for recurrent events and clustered survival data.- Causality.- First passage time models: Understanding the shape of the hazard rate.- Diffusion and L#x00E9;vy process models for dynamic frailty.
£143.99
Springer Us Density Evolution Under Delayed Dynamics
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£80.99
ISTE Ltd and John Wiley & Sons Inc Discrete Stochastic Processes and Optimal
Book SynopsisOptimal filtering applied to stationary and non-stationary signals provides the most efficient means of dealing with problems arising from the extraction of noise signals. Moreover, it is a fundamental feature in a range of applications, such as in navigation in aerospace and aeronautics, filter processing in the telecommunications industry, etc. This book provides a comprehensive overview of this area, discussing random and Gaussian vectors, outlining the results necessary for the creation of Wiener and adaptive filters used for stationary signals, as well as examining Kalman filters which are used in relation to non-stationary signals. Exercises with solutions feature in each chapter to demonstrate the practical application of these ideas using Matlab.Table of ContentsPreface xi Introduction xiii Chapter 1. Random Vectors 1 1.1. Definitions and general properties 1 1.2. Spaces L1(dP) and L2(dP) 20 1.2.1. Definitions 20 1.2.2. Properties 22 1.3. Mathematical expectation and applications 23 1.3.1. Definitions 23 1.3.2. Characteristic functions of a random vector 34 1.4. Second order random variables and vectors 39 1.5. Linear independence of vectors of L2(dP) 47 1.6. Conditional expectation (concerning random vectors with density function) 51 1.7. Exercises for Chapter 1 57 Chapter 2. Gaussian Vectors 63 2.1. Some reminders regarding random Gaussian vectors 63 2.2. Definition and characterization of Gaussian vectors 66 2.3. Results relative to independence 68 2.4. Affine transformation of a Gaussian vector 72 2.5. The existence of Gaussian vectors 74 2.6. Exercises for Chapter 2 85 Chapter 3. Introduction to Discrete Time Processes 93 3.1. Definition 93 3.2. WSS processes and spectral measure 105 3.2.1. Spectral density 106 3.3. Spectral representation of a WSS process 110 3.3.1. Problem 110 3.3.2. Results 111 3.3.2.1. Process with orthogonal increments and associated measurements 111 3.3.2.2. Wiener stochastic integral 113 3.3.2.3. Spectral representation 114 3.4. Introduction to digital filtering 115 3.5. Important example: autoregressive process 128 3.6. Exercises for Chapter 3 134 Chapter 4. Estimation 141 4.1. Position of the problem 141 4.2. Linear estimation 144 4.3. Best estimate – conditional expectation 156 4.4. Example: prediction of an autoregressive process AR (1) 165 4.5. Multivariate processes 166 4.6. Exercises for Chapter 4 175 Chapter 5. The Wiener Filter 181 5.1. Introduction 181 5.1.1. Problem position 182 5.2. Resolution and calculation of the FIR filter 183 5.3. Evaluation of the least error 185 5.4. Resolution and calculation of the IIR filter 186 5.5. Evaluation of least mean square error 190 5.6. Exercises for Chapter 5 191 Chapter 6. Adaptive Filtering: Algorithm of the Gradient and the LMS 197 6.1. Introduction 197 6.2. Position of problem 199 6.3. Data representation 202 6.4. Minimization of the cost function 204 6.4.1. Calculation of the cost function 208 6.5. Gradient algorithm 211 6.6. Geometric interpretation 214 6.7. Stability and convergence 218 6.8. Estimation of gradient and LMS algorithm 222 6.8.1. Convergence of the algorithm of the LMS 225 6.9. Example of the application of the LMS algorithm 225 6.10. Exercises for Chapter 6 234 Chapter 7. The Kalman Filter 237 7.1. Position of problem 237 7.2. Approach to estimation 241 7.2.1. Scalar case 241 7.2.2. Multivariate case 244 7.3. Kalman filtering 245 7.3.1. State equation 245 7.3.2. Observation equation 246 7.3.3. Innovation process 248 7.3.4. Covariance matrix of the innovation process 248 7.3.5. Estimation 250 7.3.6. Riccati’s equation 258 7.3.7. Algorithm and summary 260 7.4. Exercises for Chapter 7 262 Table of Symbols and Notations 281 Bibliography 283 Index 285
£137.66
Springer Nature Switzerland AG Foundations of Modern Probability
Book SynopsisThe first edition of this single volume on the theory of probability has become a highly-praised standard reference for many areas of probability theory. Chapters from the first edition have been revised and corrected, and this edition contains four new chapters. New material covered includes multivariate and ratio ergodic theorems, shift coupling, Palm distributions, Harris recurrence, invariant measures, and strong and weak ergodicity.Trade Review“The book under review is the magnum opus of a brilliant scholar of probability. … An important feature of the book … is the writing style. … The choice of topics is excellent, some of which are not covered elsewhere. This book would make an outstanding text for graduate courses in measure theoretic probability, and for graduate students and faculty doing research in probability. The book is a delight to read. Highly recommended.” (Myron Hlynka, Mathematical Reviews, April, 2022)Table of ContentsIntroduction and Reading Guide.- I.Measure Theoretic Prerequisites: 1.Sets and functions, measures and integration.- 2.Measure extension and decomposition.- 3.Kernels, disintegration, and invariance.- II.Some Classical Probability Theory: 4.Processes, distributions, and independence.- 5.Random sequences, series, and averages.- 6.Gaussian and Poisson convergence.- 7.Infinite divisibility and general null-arrays.- III.Conditioning and Martingales: 8.Conditioning and disintegration.- 9.Optional times and martingales.- 10.Predictability and compensation.- IV.Markovian and Related Structures:11.Markov properties and discrete-time chains.- 12.Random walks and renewal processes.- 13.Jump-type chains and branching processes.- V.Some Fundamental Processes: 14.Gaussian processes and Brownian motion.- 15.Poisson and related processes.- 16.Independent-increment and Lévy processes.- 17.Feller processes and semi-groups.- VI.Stochastic Calculus and Applications: 18.Itô integration and quadratic variation.- 19.Continuous martingales and Brownian motion.- 20.Semi-martingales and stochastic integration.- 21.Malliavin calculus.- VII.Convergence and Approximation: 22.Skorohod embedding and functional convergence.- 23.Convergence in distribution.- 24.Large deviations.- VIII.Stationarity, Symmetry and Invariance: 25.Stationary processes and ergodic theorems.- 26.Ergodic properties of Markov processes.- 27.Symmetric distributions and predictable maps.- 28.Multi-variate arrays and symmetries.- IX.Random Sets and Measures: 29.Local time, excursions, and additive functionals.- 30.Random mesures, smoothing and scattering.- 31.Palm and Gibbs kernels, local approximation.- X.SDEs, Diffusions, and Potential Theory: 32.Stochastic equations and martingale problems.- 33.One-dimensional SDEs and diffusions.- 34.PDE connections and potential theory.- 35.Stochastic differential geometry.- Appendices.- 1.Measurable maps.- 2.General topology.- 3.Linear spaces.- 4.Linear operators.- 5.Function and measure spaces.- 6.Classes and spaces of sets,- 7.Differential geometry.- Notes and References.- Bibliography.- Indices: Authors.- Topics.- Symbols.
£52.24
Springer International Publishing AG Continuous Time Processes for Finance: Switching, Self-exciting, Fractional and other Recent Dynamics
Book SynopsisThis book explores recent topics in quantitative finance with an emphasis on applications and calibration to time-series. This last aspect is often neglected in the existing mathematical finance literature while it is crucial for risk management. The first part of this book focuses on switching regime processes that allow to model economic cycles in financial markets. After a presentation of their mathematical features and applications to stocks and interest rates, the estimation with the Hamilton filter and Markov Chain Monte-Carlo algorithm (MCMC) is detailed. A second part focuses on self-excited processes for modeling the clustering of shocks in financial markets. These processes recently receive a lot of attention from researchers and we focus here on its econometric estimation and its simulation. A chapter is dedicated to estimation of stochastic volatility models. Two chapters are dedicated to the fractional Brownian motion and Gaussian fields. After a summary of their features, we present applications for stock and interest rate modeling. Two chapters focuses on sub-diffusions that allows to replicate illiquidity in financial markets. This book targets undergraduate students who have followed a first course of stochastic finance and practitioners as quantitative analyst or actuaries working in risk management.Trade Review“Hainaut has written a book which in such panorama has a position of its own and which should be considered with great interest. … the book should definitely be considered an excellent and warmly recommended read. It is likely that it will be soon become a reference for those interested in modern topics and for young researchers in particular.” (Gianluca Cassese, zbMATH 1512.91001, 2023)Table of ContentsPreface.- Acknowledgements.- Notations.- 1. Switching Models: Properties and Estimation.- 2. Estimation of Continuous Time Processes by Markov Chain Monte Carlo.- 3. Particle Filtering and Estimation.- 4. Modeling of Spillover Effects in Stock Markets.- 5. Non-Markov Models for Contagion and Spillover.- 6. Fractional Brownian Motion.- 7. Gaussian Fields for Asset Prices.- 8. Lévy Interest Rate Models With a Long Memory.- 9. Affine Volterra Processes and Rough Models.- 10. Sub-Diffusion for Illiquid Markets.- 11. A Fractional Dupire Equation for Jump-Diffusions.- References.
£104.49
Springer International Publishing AG The E. M. Stein Lectures on Hardy Spaces
Book SynopsisThe book The E. M. Stein Lectures on Hardy Spaces is based on a graduate course on real variable Hardy spaces which was given by E.M. Stein at Princeton University in the academic year 1973-1974. Stein, along with C. Fefferman and G. Weiss, pioneered this subject area, removing the theory of Hardy spaces from its traditional dependence on complex variables, and to reveal its real-variable underpinnings.This book is based on Steven G. Krantz’s notes from the course given by Stein. The text builds on Fefferman's theorem that BMO is the dual of the Hardy space. Using maximal functions, singular integrals, and related ideas, Stein offers many new characterizations of the Hardy spaces. The result is a rich tapestry of ideas that develops the theory of singular integrals to a new level. The final chapter describes the major developments since 1974.This monograph is of broad interest to graduate students and researchers in mathematical analysis. Prerequisites for the book include a solid understanding of real variable theory and complex variable theory. A basic knowledge of functional analysis would also be useful.Table of ContentsIntroductory material.- More on Hardy Spaces.- Background on H^p Spaces.- Hardy Spaces on D.- Hardy Spaces on R^n.- Developments Since 1974.- Concluding Remarks.- Bibliography.- Index.
£49.49
Springer International Publishing AG Random Walks on Disordered Media and their Scaling Limits: École d'Été de Probabilités de Saint-Flour XL - 2010
Book SynopsisIn these lecture notes, we will analyze the behavior of random walk on disordered media by means of both probabilistic and analytic methods, and will study the scaling limits. We will focus on the discrete potential theory and how the theory is effectively used in the analysis of disordered media. The first few chapters of the notes can be used as an introduction to discrete potential theory.Recently, there has been significant progress on the theory of random walk on disordered media such as fractals and random media. Random walk on a percolation cluster(‘the ant in the labyrinth’)is one of the typical examples. In 1986, H. Kesten showed the anomalous behavior of a random walk on a percolation cluster at critical probability. Partly motivated by this work, analysis and diffusion processes on fractals have been developed since the late eighties. As a result, various new methods have been produced to estimate heat kernels on disordered media. These developments are summarized in the notes.Table of ContentsIntroduction.- Weighted graphs and the associated Markov chains.- Heat kernel estimates – General theory.- Heat kernel estimates using effective resistance.- Heat kernel estimates for random weighted graphs.- Alexander-Orbach conjecture holds when two-point functions behave nicely.- Further results for random walk on IIC.- Random conductance model.
£29.69
Springer International Publishing AG Introduction to Uncertainty Quantification
Book SynopsisThis text provides a framework in which the main objectives of the field of uncertainty quantification (UQ) are defined and an overview of the range of mathematical methods by which they can be achieved. Complete with exercises throughout, the book will equip readers with both theoretical understanding and practical experience of the key mathematical and algorithmic tools underlying the treatment of uncertainty in modern applied mathematics. Students and readers alike are encouraged to apply the mathematical methods discussed in this book to their own favorite problems to understand their strengths and weaknesses, also making the text suitable for a self-study.Uncertainty quantification is a topic of increasing practical importance at the intersection of applied mathematics, statistics, computation and numerous application areas in science and engineering. This text is designed as an introduction to UQ for senior undergraduate and graduate students with a mathematical or statistical background and also for researchers from the mathematical sciences or from applications areas who are interested in the field.T. J. Sullivan was Warwick Zeeman Lecturer at the Mathematics Institute of the University of Warwick, United Kingdom, from 2012 to 2015. Since 2015, he is Junior Professor of Applied Mathematics at the Free University of Berlin, Germany, with specialism in Uncertainty and Risk Quantification.Trade Review“Book is one of very few that discuss a vast array of topics in the developing field of uncertainty quantification (UQ). … The text is mathematically rigorous, and though the intended audience is the senior undergraduate or early graduate mathematics student … . this is a book I might recommend to students as a reference for topics related to UQ ... . Overall, this introduction to UQ leaves something to be desired. It is well written … .” (Talea L. Mayo, SIAM Review, Vol. 59 (1), March, 2017)“This book presents a collection of mathematical results related to Uncertainly Quantification (UQ). It is intended as a textbook for senior undergraduate or graduate students with a background in mathematics and statistics. … The book might be suitable for a research seminar where students are exposed for the first time to the mathematics behind UQ.” (Elisabeth Ullmann, Mathematical Reviews, February, 2017)“This book aims to provide an introduction to the mathematics of the quantification of uncertainty. It is intended for students in mathematics and statistics. In the US this would be a graduate level textbook.” (William J. Satzer, MAA Reviews, maa.org, February, 2016)Table of ContentsIntroduction.- Measure and Probability Theory.- Banach and Hilbert Spaces.- Optimization Theory.- Measures of Information and Uncertainty.- Bayesian Inverse Problems.- Filtering and Data Assimilation.- Orthogonal Polynomials and Applications.- Numerical Integration.- Sensitivity Analysis and Model Reduction.- Spectral Expansions.- Stochastic Galerkin Methods.- Non-Intrusive Methods.- Distributional Uncertainty.- References.- Index.
£56.24
Springer International Publishing AG Markov Chains
Book SynopsisThis book covers the classical theory of Markov chains on general state-spaces as well as many recent developments. The theoretical results are illustrated by simple examples, many of which are taken from Markov Chain Monte Carlo methods. The book is self-contained, while all the results are carefully and concisely proven. Bibliographical notes are added at the end of each chapter to provide an overview of the literature. Part I lays the foundations of the theory of Markov chain on general states-space. Part II covers the basic theory of irreducible Markov chains on general states-space, relying heavily on regeneration techniques. These two parts can serve as a text on general state-space applied Markov chain theory. Although the choice of topics is quite different from what is usually covered, where most of the emphasis is put on countable state space, a graduate student should be able to read almost all these developments without any mathematical background deeper than that needed to study countable state space (very little measure theory is required). Part III covers advanced topics on the theory of irreducible Markov chains. The emphasis is on geometric and subgeometric convergence rates and also on computable bounds. Some results appeared for a first time in a book and others are original. Part IV are selected topics on Markov chains, covering mostly hot recent developments.Table of ContentsPart I Foundations.- Markov Chains: Basic Definitions.- Examples of Markov Chains.- Stopping Times and the Strong Markov Property.- Martingales, Harmonic Functions and Polsson-Dirichlet Problems.- Ergodic Theory for Markov Chains.- Part II Irreducible Chains: Basics.- Atomic Chains.- Markov Chains on a Discrete State Space.- Convergence of Atomic Markov Chains.- Small Sets, Irreducibility and Aperiodicity.- Transience, Recurrence and Harris Recurrence.- Splitting Construction and Invariant Measures.- Feller and T-kernels.- Part III Irreducible Chains: Advanced Topics.- Rates of Convergence for Atomic Markov Chains.- Geometric Recurrence and Regularity.- Geometric Rates of Convergence.- (f, r)-recurrence and Regularity.- Subgeometric Rates of Convergence.- Uniform and V-geometric Ergodicity by Operator Methods.- Coupling for Irreducible Kernels.- Part IV Selected Topics.- Convergence in the Wasserstein Distance.- Central Limit Theorems.- Spectral Theory.- Concentration Inequalities.- Appendices.- A Notations.- B Topology, Measure, and Probability.- C Weak Convergence.- D Total and V-total Variation Distances.- E Martingales.- F Mixing Coefficients.- G Solutions to Selected Exercises.
£67.49
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Probability Essentials
Book SynopsisThis introduction can be used, at the beginning graduate level, for a one-semester course on probability theory or for self-direction without benefit of a formal course; the measure theory needed is developed in the text. It will also be useful for students and teachers in related areas such as finance theory, electrical engineering, and operations research. The text covers the essentials in a directed and lean way with 28 short chapters, and assumes only an undergraduate background in mathematics. Readers are taken right up to a knowledge of the basics of Martingale Theory, and the interested student will be ready to continue with the study of more advanced topics, such as Brownian Motion and Ito Calculus, or Statistical Inference.Trade Review"(The book is) a lean and largely self-contained introduction to the modern theory of probability, aimed at advanced undergraduate or beginning graduate students. The 28 short chapters belie the book's genesis as polished lecture notes; the exposition is sleek and rigorous and each chapter ends with a supporting collection of mainly routine exercises. ... The authors make it clear what luggage is required for this exhilarating trek,... a good knowledge of advanced calculus, some linear algebra, and some "mathematical sophistication". With this understood, the itinerary is immaculately paced and planned with just the right balances of technical ascents and pauses to admire the scenery. Within the constraints of a slim volume, it is hard to imagine how the authors could have done a more effective or more attractive job." The Mathematical Gazette, Vol. 84, No 500, 2000 "The authors provide the shortest path through the twenty-eight chapter headings. The topics are treated in a mathematically and pedagogically digestible way. The writing is concise and crisp: the average chapter length is about eight pages. ... Numerous exercises add to the value of the text as a teaching tool. In conclusion, this is an excellent text for the intended audience."Short Book Reviews, Vol. 21, No. 2, 2001Table of Contents1. Introduction 2. Axioms of Probability 3. Conditional Probability and Independence 4. Probabilities on a Countable Space 5. Random Variables on a Countable Space 6. Construction of a Probability Measure 7. Construction of a Probability Measure on R 8. Random Variables 9. Integration with Respect to a Probability Measure 10. Independent Random Variables 11. Probability Distributions on R 12. Probability Distributions on Rn 13. Characteristic Functions 14. Properties of Characteristic Functions 15. Sums of Independent Random Variables 16. Gaussian Random Variables (The Normal and the Multivariate Normal Distributions) 17. Convergence of Random Variables 18. Weak Convergence 19. Weak Convergence and Characteristic Functions 20. The Laws of Large Numbers 21. The Central Limit Theorem 22. L2 and Hilbert Spaces 23. Conditional Expectation 24. Martingales 25. Supermartingales and Submartingales 26. Martingale Inequalities 27. Martingales Convergence Theorems 28. The Radon-Nikodym Theorem
£52.24
Springer Verlag, Japan Stochastic Control Theory: Dynamic Programming
Book SynopsisThis book offers a systematic introduction to the optimal stochastic control theory via the dynamic programming principle, which is a powerful tool to analyze control problems.First we consider completely observable control problems with finite horizons. Using a time discretization we construct a nonlinear semigroup related to the dynamic programming principle (DPP), whose generator provides the Hamilton–Jacobi–Bellman (HJB) equation, and we characterize the value function via the nonlinear semigroup, besides the viscosity solution theory. When we control not only the dynamics of a system but also the terminal time of its evolution, control-stopping problems arise. This problem is treated in the same frameworks, via the nonlinear semigroup. Its results are applicable to the American option price problem.Zero-sum two-player time-homogeneous stochastic differential games and viscosity solutions of the Isaacs equations arising from such games are studied via a nonlinear semigroup related to DPP (the min-max principle, to be precise). Using semi-discretization arguments, we construct the nonlinear semigroups whose generators provide lower and upper Isaacs equations.Concerning partially observable control problems, we refer to stochastic parabolic equations driven by colored Wiener noises, in particular, the Zakai equation. The existence and uniqueness of solutions and regularities as well as Itô's formula are stated. A control problem for the Zakai equations has a nonlinear semigroup whose generator provides the HJB equation on a Banach space. The value function turns out to be a unique viscosity solution for the HJB equation under mild conditions.This edition provides a more generalized treatment of the topic than does the earlier book Lectures on Stochastic Control Theory (ISI Lecture Notes 9), where time-homogeneous cases are dealt with. Here, for finite time-horizon control problems, DPP was formulated as a one-parameter nonlinear semigroup, whose generator provides the HJB equation, by using a time-discretization method. The semigroup corresponds to the value function and is characterized as the envelope of Markovian transition semigroups of responses for constant control processes. Besides finite time-horizon controls, the book discusses control-stopping problems in the same frameworks.
£98.99
Birkhauser Verlag AG Doubly Stochastic Models for Volcanic Hazard
Book SynopsisThis study provides innovative mathematical models for assessing the eruption probability and associated volcanic hazards, and applies them to the Campi Flegrei caldera in Italy. Throughout the book, significant attention is devoted to quantifying the sources of uncertainty affecting the forecast estimates. The Campi Flegrei caldera is certainly one of the world’s highest-risk volcanoes, with more than 70 eruptions over the last 15,000 years, prevalently explosive ones of varying magnitude, intensity and vent location. In the second half of the twentieth century the volcano apparently once again entered a phase of unrest that continues to the present. Hundreds of thousands of people live inside the caldera and over a million more in the nearby city of Naples, making a future eruption of Campi Flegrei an event with potentially catastrophic consequences at the national and European levels.Table of ContentsIntroduction.- Vent opening probability maps.- Pyroclastic density current invasion maps.- Time-space model for the next eruption.- Addendum.- Supporting information.
£16.14
World Scientific Publishing Co Pte Ltd Informal Introduction To Stochastic Calculus With
Book SynopsisMost branches of science involving random fluctuations can be approached by Stochastic Calculus. These include, but are not limited to, signal processing, noise filtering, stochastic control, optimal stopping, electrical circuits, financial markets, molecular chemistry, population dynamics, etc. All these applications assume a strong mathematical background, which in general takes a long time to develop. Stochastic Calculus is not an easy to grasp theory, and in general, requires acquaintance with the probability, analysis and measure theory.The goal of this book is to present Stochastic Calculus at an introductory level and not at its maximum mathematical detail. The author's goal was to capture as much as possible the spirit of elementary deterministic Calculus, at which students have been already exposed. This assumes a presentation that mimics similar properties of deterministic Calculus, which facilitates understanding of more complicated topics of Stochastic Calculus.The second edition contains several new features that improved the first edition both qualitatively and quantitatively. First, two more chapters have been added, Chapter 12 and Chapter 13, dealing with applications of stochastic processes in Electrochemistry and global optimization methods.This edition contains also a final chapter material containing fully solved review problems and provides solutions, or at least valuable hints, to all proposed problems. The present edition contains a total of about 250 exercises.This edition has also improved presentation from the first edition in several chapters, including new material.
£63.00
The University of Chicago Press Dimension Theory in Dynamical Systems
Book SynopsisThe principles of symmetry and self-symmetry are expressed in fractals, the subject of study in dimension theory. This book introduces an area of research which has recently appeared in the interface between dimension theory and the theory of dynamical systems, focusing on invariant fractals.
£30.40
Springer-Verlag New York Inc. All of Statistics
Book SynopsisTaken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.Trade ReviewWinner of the 2005 DeGroot Prize.From the reviews:"Presuming no previous background in statistics and described by the author as "demanding" yet "understandable because the material is as intuitive as possible" (p. viii), this certainly would be my choice of textbook if I was required to learn mathematical statistics again for a couple of semesters." Technometrics, August 2004"This book should be seriously considered as a text for a theoretical statsitics course for non-majors, and perhaps even for majors...The coverage of emerging and important topics is timely and welcomed...you should have this book on your desk as a reference to nothing less than 'All of Statistics.'" Biometrics, December 2004"Although All of Statistics is an ambitious title, this book is a concise guide, as the subtitle suggests....I recommend it to anyone who has an interest in learning something new about statistical inference. There is something here for everyone." The American Statistician, May 2005"As the title of the book suggests, ‘All of Statistics’ covers a wide range of statistical topics. … The number of topics covered in this book is vast … . The greatest strength of this book is as a first point of reference for a wide range of statistical methods. … I would recommend this book as a useful and interesting introduction to a large number of statistical topics for non-statisticians and also as a useful reference book for practicing statisticians." (Matthew J. Langdon, Journal of Applied Statistics, Vol. 32 (1), January, 2005)"This book was written specifically to give students a quick but sound understanding of modern statistics, and its coverage is very wide. … The book is extremely well done … ." (N. R. Draper, Short Book Reviews, Vol. 24 (2), 2004)"This is most definitely a book about mathematical statistics. It is full of theorems and proofs … . Presuming no previous background in statistics … this certainly would be my choice of textbook if I was required to learn mathematical statistics again for a couple of semesters." (Eric R. Ziegel, Technometrics, Vol. 46 (3), August, 2004)"The author points out that this book is for those who wish to learn probability and statistics quickly … . this book will serve as a guideline for instructors as to what should constitute a basic education in modern statistics. It introduces many modern topics … . Adequate references are provided at the end of each chapter which the instructor will be able to use profitably … ." (Arup Bose, Sankhya, Vol. 66 (3), 2004)"The amount of material that is covered in this book is impressive. … the explanations are generally clear and the wide range of techniques that are discussed makes it possible to include a diverse set of examples … . The worked examples are complemented with numerous theoretical and practical exercises … . is a very useful overview of many areas of modern statistics and as such will be very useful to readers who require such a survey. Library copies would also see plenty of use." (Stuart Barber, Journal of the Royal Statistical Society, Series A – Statistics in Society, Vol. 168 (1), 2005)Table of ContentsProbability.- Random Variables.- Expectation.- Inequalities.- Convergence of Random Variables.- Models, Statistical Inference and Learning.- Estimating the CDF and Statistical Functionals.- The Bootstrap.- Parametric Inference.- Hypothesis Testing and p-values.- Bayesian Inference.- Statistical Decision Theory.- Linear and Logistic Regression.- Multivariate Models.- Inference about Independence.- Causal Inference.- Directed Graphs and Conditional Independence.- Undirected Graphs.- Loglinear Models.- Nonparametric Curve Estimation.- Smoothing Using Orthogonal Functions.- Classification.- Probability Redux: Stochastic Processes.- Simulation Methods.
£53.99
John Wiley & Sons Inc LQ Dynamic Optimization and Differential Games
Book SynopsisLinear Quadratic Differential Games is an assessment of the state of the art in its field and modern book on linear-quadratic game theory, one of the most commonly used tools for modelling and analysing strategic decision making problems in economics and management.Table of ContentsPreface. Notation and symbols. 1 Introduction. 1.1 Historical perspective. 1.2 How to use this book. 1.3 Outline of this book. 1.4 Notes and references. 2 Linear algebra. 2.1 Basic concepts in linear algebra. 2.2 Eigenvalues and eigenvectors. 2.3 Complex eigenvalues. 2.4 Cayley–Hamilton theorem. 2.5 Invariant subspaces and Jordan canonical form. 2.6 Semi-definite matrices. 2.7 Algebraic Riccati equations. 2.8 Notes and references. 2.9 Exercises. 2.10 Appendix. 3 Dynamical systems. 3.1 Description of linear dynamical systems. 3.2 Existence–uniqueness results for differential equations. 3.2.1 General case. 3.2.2 Control theoretic extensions. 3.3 Stability theory: general case. 3.4 Stability theory of planar systems. 3.5 Geometric concepts. 3.6 Performance specifications. 3.7 Examples of differential games. 3.8 Information, commitment and strategies. 3.9 Notes and references. 3.10 Exercises. 3.11 Appendix. 4 Optimization techniques. 4.1 Optimization of functions. 4.2 The Euler–Lagrange equation. 4.3 Pontryagin’s maximum principle. 4.4 Dynamic programming principle. 4.5 Solving optimal control problems. 4.6 Notes and references. 4.7 Exercises. 4.8 Appendix. 5 Regular linear quadratic optimal control. 5.1 Problem statement. 5.2 Finite-planning horizon. 5.3 Riccati differential equations. 5.4 Infinite-planning horizon. 5.5 Convergence results. 5.6 Notes and references. 5.7 Exercises. 5.8 Appendix. 6 Cooperative games. 6.1 Pareto solutions. 6.2 Bargaining concepts. 6.3 Nash bargaining solution. 6.4 Numerical solution. 6.5 Notes and references. 6.6 Exercises. 6.7 Appendix. 7 Non-cooperative open-loop information games. 7.1 Introduction. 7.2 Finite-planning horizon. 7.3 Open-loop Nash algebraic Riccati equations. 7.4 Infinite-planning horizon. 7.5 Computational aspects and illustrative examples. 7.6 Convergence results. 7.7 Scalar case. 7.8 Economics examples. 7.8.1 A simple government debt stabilization game. 7.8.2 A game on dynamic duopolistic competition. 7.9 Notes and references. 7.10 Exercises. 7.11 Appendix. 8 Non-cooperative feedback information games. 8.1 Introduction. 8.2 Finite-planning horizon. 8.3 Infinite-planning horizon. 8.4 Two-player scalar case. 8.5 Computational aspects. 8.5.1 Preliminaries. 8.5.2 A scalar numerical algorithm: the two-player case. 8.5.3 The N-player scalar case. 8.6 Convergence results for the two-player scalar case. 8.7 Notes and references. 8.8 Exercises. 8.9 Appendix. 9 Uncertain non-cooperative feedback information games. 9.1 Stochastic approach. 9.2 Deterministic approach: introduction. 9.3 The one-player case. 9.4 The one-player scalar case. 9.5 The two-player case. 9.6 A fishery management game. 9.7 A scalar numerical algorithm. 9.8 Stochastic interpretation. 9.9 Notes and references. 9.10 Exercises. 9.11 Appendix. References. Index.
£101.66
John Wiley & Sons Inc Stochastic Geometry and Its Applications
Book SynopsisAn extensive update to a classic text Stochastic geometry and spatial statistics play a fundamental role in many modern branches of physics, materials sciences, engineering, biology and environmental sciences.Table of ContentsForeword to the first edition xiii From the preface to the first edition xvii Preface to the second edition xix Preface to the third edition xxi Notation xxiii 1 Mathematical foundations 1 1.1 Set theory 1 1.2 Topology in Euclidean spaces 3 1.3 Operations on subsets of Euclidean space 5 1.4 Mathematical morphology and image analysis 7 1.5 Euclidean isometries 9 1.6 Convex sets in Euclidean spaces 10 1.7 Functions describing convex sets 17 1.8 Polyconvex sets 24 1.9 Measure and integration theory 27 2 Point processes I: The Poisson point process 35 2.1 Introduction 35 2.2 The binomial point process 36 2.3 The homogeneous Poisson point process 41 2.4 The inhomogeneous and general Poisson point process 51 2.5 Simulation of Poisson point processes 53 2.6 Statistics for the homogeneous Poisson point process 55 3 Random closed sets I: The Boolean model 64 3.1 Introduction and basic properties 64 3.2 The Boolean model with convex grains 78 3.3 Coverage and connectivity 89 3.4 Statistics 95 3.5 Generalisations and variations 103 3.6 Hints for practical applications 106 4 Point processes II: General theory 108 4.1 Basic properties 108 4.2 Marked point processes 116 4.3 Moment measures and related quantities 120 4.4 Palm distributions 127 4.5 The second moment measure 139 4.6 Summary characteristics 143 4.7 Introduction to statistics for stationary spatial point processes 145 4.8 General point processes 156 5 Point processes III: Models 158 5.1 Operations on point processes 158 5.2 Doubly stochastic Poisson processes (Cox processes) 166 5.3 Neyman–Scott processes 171 5.4 Hard-core point processes 176 5.5 Gibbs point processes 178 5.6 Shot-noise fields 200 6 Random closed sets II: The general case 205 6.1 Basic properties 205 6.2 Random compact sets 213 6.3 Characteristics for stationary and isotropic random closed sets 216 6.4 Nonparametric statistics for stationary random closed sets 230 6.5 Germ–grain models 237 6.6 Other random closed set models 255 6.7 Stochastic reconstruction of random sets 276 7 Random measures 279 7.1 Fundamentals 279 7.2 Moment measures and related characteristics 284 7.3 Examples of random measures 286 8 Line, fibre and surface processes 297 8.1 Introduction 297 8.2 Flat processes 302 8.3 Planar fibre processes 314 8.4 Spatial fibre processes 330 8.5 Surface processes 333 8.6 Marked fibre and surface processes 339 9 Random tessellations, geometrical networks and graphs 343 9.1 Introduction and definitions 343 9.2 Mathematical models for random tessellations 346 9.3 General ideas and results for stationary planar tessellations 357 9.4 Mean-value formulae for stationary spatial tessellations 367 9.5 Poisson line and plane tessellations 370 9.6 STIT tessellations 375 9.7 Poisson-Voronoi and Delaunay tessellations 376 9.8 Laguerre tessellations 386 9.9 Johnson–Mehl tessellations 388 9.10 Statistics for stationary tessellations 390 9.11 Random geometrical networks 397 9.12 Random graphs 402 10 Stereology 411 10.1 Introduction 411 10.2 The fundamental mean-value formulae of stereology 413 10.3 Stereological mean-value formulae for germ–grain models 421 10.4 Stereological methods for spatial systems of balls 425 10.5 Stereological problems for nonspherical grains (shape-and-size problems) 436 10.6 Stereology for spatial tessellations 440 10.7 Second-order characteristics and directional distributions 444 References 453 Author index 507 Subject index 521
£76.90
John Wiley & Sons Inc Stochastic Claims Reserving Methods in Insurance
Book SynopsisCovers all the theory and practical advice that actuaries need in order to determine the claims reserves for non-life insurance. Describes all the necessary mathematical methods used to estimate loss reserves and shares the authors' practical experience, which is essential in showing which of the methods should be applied in any given situation.Table of ContentsPreface xi Acknowledgement xiii 1 Introduction and Notation 1 1.1 Claims process 1 1.1.1 Accounting principles and accident years 2 1.1.2 Inflation 3 1.2 Structural framework to the claims-reserving problem 5 1.2.1 Fundamental properties of the claims reserving process 7 1.2.2 Known and unknown claims 9 1.3 Outstanding loss liabilities, classical notation 10 1.4 General remarks 12 2 Basic Methods 15 2.1 Chain-ladder method (distribution-free) 15 2.2 Bornhuetter–Ferguson method 21 2.3 Number of IBNyR claims, Poisson model 25 2.4 Poisson derivation of the CL algorithm 27 3 Chain-Ladder Models 33 3.1 Mean square error of prediction 33 3.2 Chain-ladder method 36 3.2.1 Mack model (distribution-free CL model) 37 3.2.2 Conditional process variance 41 3.2.3 Estimation error for single accident years 44 3.2.4 Conditional MSEP, aggregated accident years 55 3.3 Bounds in the unconditional approach 58 3.3.1 Results and interpretation 58 3.3.2 Aggregation of accident years 63 3.3.3 Proof of Theorems 3.17, 3.18 and 3.20 64 3.4 Analysis of error terms in the CL method 70 3.4.1 Classical CL model 70 3.4.2 Enhanced CL model 71 3.4.3 Interpretation 72 3.4.4 CL estimator in the enhanced model 73 3.4.5 Conditional process and parameter prediction errors 74 3.4.6 CL factors and parameter estimation error 75 3.4.7 Parameter estimation 81 4 Bayesian Models 91 4.1 Benktander–Hovinen method and Cape–Cod model 91 4.1.1 Benktander–Hovinen method 92 4.1.2 Cape–Cod model 95 4.2 Credible claims reserving methods 98 4.2.1 Minimizing quadratic loss functions 98 4.2.2 Distributional examples to credible claims reserving 101 4.2.3 Log-normal/Log-normal model 105 4.3 Exact Bayesian models 113 4.3.1 Overdispersed Poisson model with gamma prior distribution 114 4.3.2 Exponential dispersion family with its associated conjugates 122 4.4 Markov chain Monte Carlo methods 131 4.5 Bühlmann–Straub credibility model 145 4.6 Multidimensional credibility models 154 4.6.1 Hachemeister regression model 155 4.6.2 Other credibility models 159 4.7 Kalman filter 160 5 Distributional Models 167 5.1 Log-normal model for cumulative claims 167 5.1.1 Known variances σj 2 170 5.1.2 Unknown variances 177 5.2 Incremental claims 182 5.2.1 (Overdispersed) Poisson model 182 5.2.2 Negative-Binomial model 183 5.2.3 Log-normal model for incremental claims 185 5.2.4 Gamma model 186 5.2.5 Tweedie’s compound Poisson model 188 5.2.6 Wright’s model 199 6 Generalized Linear Models 201 6.1 Maximum likelihood estimators 201 6.2 Generalized linear models framework 203 6.3 Exponential dispersion family 205 6.4 Parameter estimation in the EDF 208 6.4.1 MLE for the EDF 208 6.4.2 Fisher’s scoring method 210 6.4.3 Mean square error of prediction 214 6.5 Other GLM models 223 6.6 Bornhuetter–Ferguson method, revisited 223 6.6.1 MSEP in the BF method, single accident year 226 6.6.2 MSEP in the BF method, aggregated accident years 230 7 Bootstrap Methods 233 7.1 Introduction 233 7.1.1 Efron’s non-parametric bootstrap 234 7.1.2 Parametric bootstrap 236 7.2 Log-normal model for cumulative sizes 237 7.3 Generalized linear models 242 7.4 Chain-ladder method 244 7.4.1 Approach 1: Unconditional estimation error 246 7.4.2 Approach 3: Conditional estimation error 247 7.5 Mathematical thoughts about bootstrapping methods 248 7.6 Synchronous bootstrapping of seemingly unrelated regressions 253 8 Multivariate Reserving Methods 257 8.1 General multivariate framework 257 8.2 Multivariate chain-ladder method 259 8.2.1 Multivariate CL model 259 8.2.2 Conditional process variance 264 8.2.3 Conditional estimation error for single accident years 265 8.2.4 Conditional MSEP, aggregated accident years 272 8.2.5 Parameter estimation 274 8.3 Multivariate additive loss reserving method 288 8.3.1 Multivariate additive loss reserving model 288 8.3.2 Conditional process variance 295 8.3.3 Conditional estimation error for single accident years 295 8.3.4 Conditional MSEP, aggregated accident years 297 8.3.5 Parameter estimation 299 8.4 Combined Multivariate CL and ALR method 308 8.4.1 Combined CL and ALR method: the model 308 8.4.2 Conditional cross process variance 313 8.4.3 Conditional cross estimation error for single accident years 315 8.4.4 Conditional MSEP, aggregated accident years 319 8.4.5 Parameter estimation 321 9 Selected Topics I: Chain-Ladder Methods 331 9.1 Munich chain-ladder 331 9.1.1 The Munich chain-ladder model 333 9.1.2 Credibility approach to the MCL method 335 9.1.3 MCL Parameter estimation 340 9.2 CL Reserving: A Bayesian inference model 346 9.2.1 Prediction of the ultimate claim 351 9.2.2 Likelihood function and posterior distribution 351 9.2.3 Mean square error of prediction 354 9.2.4 Credibility chain-ladder 359 9.2.5 Examples 361 9.2.6 Markov chain Monte Carlo methods 364 10 Selected Topics II: Individual Claims Development Processes 369 10.1 Modelling claims development processes for individual claims 369 10.1.1 Modelling framework 370 10.1.2 Claims reserving categories 376 10.2 Separating IBNeR and IBNyR claims 379 11 Statistical Diagnostics 391 11.1 Testing age-to-age factors 391 11.1.1 Model choice 394 11.1.2 Age-to-age factors 396 11.1.3 Homogeneity in time and distributional assumptions 398 11.1.4 Correlations 399 11.1.5 Diagonal effects 401 11.2 Non-parametric smoothing 401 Appendix A: Distributions 405 A.1 Discrete distributions 405 A.1.1 Binomial distribution 405 A.1.2 Poisson distribution 405 A.1.3 Negative-Binomial distribution 405 A.2 Continuous distributions 406 A.2.1 Uniform distribution 406 A.2.2 Normal distribution 406 A.2.3 Log-normal distribution 407 A.2.4 Gamma distribution 407 A.2.5 Beta distribution 408 Bibliography 409 Index 417
£78.38
John Wiley & Sons Inc Probability Statistics and Stochastic Processes
Book SynopsisPraise for the First Edition . . . an excellent textbook . . . well organized and neatly written. Mathematical Reviews . . . amazingly interesting . . . Technometrics Thoroughly updated to showcase the interrelationships between probability, statistics, and stochastic processes, Probability, Statistics, and Stochastic Processes, Second Edition prepares readers to collect, analyze, and characterize data in their chosen fields. Beginning with three chapters that develop probability theory and introduce the axioms of probability, random variables, and joint distributions, the book goes on to present limit theorems and simulation. The authors combine a rigorous, calculus-based development of theory with an intuitive approach that appeals to readers'' sense of reason and logic. Including more than 400 examples that help illustrate concepts and theory, the Second Edition features new material on statiTable of ContentsPreface xi Preface to the First Edition xiii 1 Basic Probability Theory 1 1.1 Introduction 1 1.2 Sample Spaces and Events 3 1.3 The Axioms of Probability 7 1.4 Finite Sample Spaces and Combinatorics 15 1.4.1 Combinatorics 17 1.5 Conditional Probability and Independence 27 1.6 The Law of Total Probability and Bayes’ Formula 41 Problems 63 2 Random Variables 76 2.1 Introduction 76 2.2 Discrete Random Variables 77 2.3 Continuous Random Variables 82 2.4 Expected Value and Variance 95 2.5 Special Discrete Distributions 111 2.6 The Exponential Distribution 123 2.7 The Normal Distribution 127 2.8 Other Distributions 131 2.9 Location Parameters 137 2.10 The Failure Rate Function 139 Problems 144 3 Joint Distributions 156 3.1 Introduction 156 3.2 The Joint Distribution Function 156 3.3 Discrete Random Vectors 158 3.4 Jointly Continuous Random Vectors 160 3.5 Conditional Distributions and Independence 164 3.5.1 Independent Random Variables 168 3.6 Functions of Random Vectors 172 3.7 Conditional Expectation 185 3.8 Covariance and Correlation 196 3.9 The Bivariate Normal Distribution 209 3.10 Multidimensional Random Vectors 216 3.11 Generating Functions 231 3.12 The Poisson Process 240 Problems 247 4 Limit Theorems 263 4.1 Introduction 263 4.2 The Law of Large Numbers 264 4.3 The Central Limit Theorem 268 4.4 Convergence in Distribution 275 Problems 278 5 Simulation 281 5.1 Introduction 281 5.2 Random Number Generation 282 5.3 Simulation of Discrete Distributions 283 5.4 Simulation of Continuous Distributions 285 5.5 Miscellaneous 290 Problems 292 6 Statistical Inference 294 6.1 Introduction 294 6.2 Point Estimators 294 6.3 Confidence Intervals 304 6.4 Estimation Methods 312 6.5 Hypothesis Testing 327 6.6 Further Topics in Hypothesis Testing 334 6.7 Goodness of Fit 339 6.8 Bayesian Statistics 351 6.9 Nonparametric Methods 363 Problems 378 7 Linear Models 391 7.1 Introduction 391 7.2 Sampling Distributions 392 7.3 Single Sample Inference 395 7.4 Comparing Two Samples 402 7.5 Analysis of Variance 409 7.6 Linear Regression 415 7.7 The General Linear Model 431 Problems 436 8 Stochastic Processes 444 8.1 Introduction 444 8.2 Discrete -Time Markov Chains 445 8.3 Random Walks and Branching Processes 464 8.4 Continuous -Time Markov Chains 475 8.5 Martingales 494 8.6 Renewal Processes 502 8.7 Brownian Motion 509 Problems 517 Appendix A Tables 527 Appendix B Answers to Selected Problems 535 Further Reading 551 Index 553
£102.56
John Wiley & Sons Inc Operational Subjective Statistical Methods
Book SynopsisMethods of subjective statistical analysis have seen a resurgence of activity in the last decade. This book treats the theory of probability and the logic of uncertainty in a systematic way. It features a technical presentation of the mathematical impact of personal beliefs and values on statistical analysis.Trade Review"...has a merit for everyone who wonders about the foundations ofinference..." (Australian & New Zealand J Statistics, 2000)Table of ContentsPhilosophical and Historical Introduction. Quantities, Prevision, and Coherency. Coherent Statistical Inference. Related Forms for Asserting Uncertain Knowledge. Distribution Functions. Proper Scoring Rules. The Multivariate Normal Distribution and Its Mixtures. Sequential Forecasting Based on Linear Conditional PrevisionStructures: Theory and Practice of Linear Regression. The Direction of Statistical Research. References. Index.
£157.45
John Wiley & Sons Inc Statistical Methods in Analytical Chemistry
Book SynopsisThis edition continues to provide analytical chemists and statisticians with the latest practical information on using statistical tools in chemical data analysis. The accompanying FTP site contains a series of programs that illustrate the statistical techniques which are discussed in the book.Trade Review“This new edition of a successful, bestselling book continues to provide you with practical information on the useof statistical methods for solving real-world problems in complex industrial environments.” (PDFCHM Online, 27 February 2013) "...a comprehensive, very useful and clear guide for all analytical chemists..." (Annali di Chimica, Vol 153, 2000) "Substantially updated...for lab supervisors and project mangers, and is useful...for advanced students of chemistry and pharmaceutical science." (SciTech Book News, Vol. 24, No. 2, June 2001) "Its clarity, focus and logical approach to statistical analysis of chemical data make it a book that should appear on the bookshelf of most analytical chemists." (Journal of the American Chemical Society, Vol. 123 No. 36)Table of ContentsUnivariate Data. Bi- and Multivariate Data. Related Topics. Complex Examples. Appendices. Technical Tidbits. Glossary. References. Index.
£175.46
John Wiley & Sons Inc Numerical Methods for Stochastic Processes
Book SynopsisThis study deals with the calculations of mathematical expectations, primarily by simulation methods. The authors explore the present state of research and signal the types of problems raised by new methods. Topics discussed include Monte Carlo methods and the simulation of stochastic processes.Table of ContentsPreliminaries. Computation of Expectations in Finite Dimension. Simulation of Random Processes. Deterministic Resolution of Some Markovian Problems. Stochastic Differential Equations and Brownian Functionals. Notes. References. Index.
£184.46
John Wiley & Sons Inc Markov Processes
Book SynopsisThe Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. [A]nyone who works with Markov processes whose state space is uncountably infinite will need this most impressive book as a guide and reference. -American Scientist There is no question but that space should immediately be reserved for [this] book on the library shelf. Those who aspire to mastery of the contents should also reserve a large number of long winter evenings. -Zentralblatt für Mathematik und ihre Grenzgebiete/Mathematics Abstracts Ethier and Kurtz have produced an excellent treatment of the modern theory of Markov processes that [is] useful both as a reference wTable of ContentsIntroduction. 1. Operator Semigroups. 2. Stochastic Processes and Martingales. 3. Convergence of Probability Measures. 4. Generators and Markov Processes. 5. Stochastic Integral Equations. 6. Random Time Changes. 7. Invariance Principles and Diffusion Approximations. 8. Examples of Generators. 9. Branching Processes. 10. Genetic Models. 11. Density Dependent Population Processes. 12. Random Evolutions. Appendixes. References. Index. Flowchart.
£107.06
John Wiley & Sons Inc Counting Processes and Survival Analysis
Book SynopsisThis book explores the martingale approach to the statistical analysis of counting processes, with an emphasis on application of those methods to censored failure time data. Introduced in the 1970s, this approach has proven to be remarkably successful in yielding results about statistical methods for many problems arising in censored data.Trade Review"…a unique source for combining the theory and the application of the survival analysis with censored data." (Technometrics, August 2007)Table of ContentsPreface. 0. The Applied Setting. 1. The Counting Process and Martingale Framework. 2. Local Square Integrable Martingales. 3. Finite Sample Moments and Large Sample Consistency of Tests and Estimators. 4. Censored Data Regression Models and Their Application. 5. Martingale Central Limit Theorem. 6. Large Sample results of the Kaplan-Meier Estimator. 7. Weighted Logrank Statistics. 8. Distribution Theory for Proportional Hazards Regression. Appendix A: Some Results from stieltjes Integration and Probability Theory. Appendix B: An Introduction to Weak convergence. Appendix C: The Martingale Central Limit Theorem: Some Preliminaries. Appendix D: Data. Appendix E: Exercises. Bibliography. Notation. Author Index. Subject Index.
£101.66
John Wiley & Sons Inc Practical Statistics for Experimental Biologists
Book SynopsisA good working knowledge of statistical principles is needed for both the design and analysis of biological experiments and the subsequent handling of the large amounts of data generated if worthwhile, reliable conclusions are to be reached. Practical Statistics for Experimental Biologists, Second Edition provides biologists with a user-friendly, non-technical introduction to the basics of statistics. The book has been thoroughly revised and updated to incorporate: * Worked examples and printouts from MINITAB * Relevant case studies and applications * Further Notes section for background explanations Written by a biologist with extensive experience of applying statistical procedures to experimental systems, this book will be invaluable to undergraduates, postgraduates and researchers in microbiology, immunology, biochemistry, botany, zoology, physiology, pharmacology and pharmacy. Review of the First Edition ...strongly recommended as the current first choicTrade Review"...a refreshing and useful book..." ---- Trends in Plant Science, September 2000Table of ContentsA Simple Experiment in Pipetting. How to Condense the Bulkiness of Data. Are Those Differences Significant? More About Measurement Differences. Awkward-Measurement Data. How to Deal with Count Data. How to Deal with Proportion Data. Correlation and Regression. Dose-Response Lines and Assays. References. Additional Reading. Appendices. Index.
£62.96
Harvard University Press Randomness
Book SynopsisThis book is aimed at the trouble with trying to learn about probability. A story of the misconceptions and difficulties civilization overcame in progressing toward probabilistic thinking, Randomness is also a skillful account of what makes the science of probability so daunting in our own day.Trade ReviewClearly, the computation of probabilities is not just an arid game… As Deborah Bennett shows in her excellent little book on the mathematics of chance, the concept has been controversial for thousands of years… [Her] cultured and accessible book goes a long way towards demystifying the science of probability and thereby offers the reader a useful variety of conceptual tools with which to probe the future and illuminate the present. -- Steven Poole * The Guardian *[Randomness] can most easily be described as a brief history of chance… I can cheerfully recommend it to anyone who is a total beginner when it comes to probability, what it means, why it is desperately puzzling, and what it can do for us despite that… It is fascinating to read about the pioneers of probability, such as Pierre Simon de Laplace with his ‘normal distribution’—now more familiar as the notorious bell curve—and Adolphe Quetelet, perhaps the first to realise that there are statistical patterns in human behaviour. And I applaud the blunt reminder that when it comes to the real world the ‘normal’ distribution is actually highly abnormal… My main criticism: it left me wanting more. A sequel, please. -- Ian Stewart * Times Higher Education Supplement *Chances are high that reading this book will clear up your misconceptions about randomness and probabilities. In this very entertaining little book, simply written but intended for careful readers, some of the most common mistakes people make about chance are carefully analyzed. While describing interesting aspects of the mathematics of probability, the author takes frequent detours into the history of humanity’s understanding (and misunderstanding) of the laws of chance, touching on subjects as diverse as chance in decision-making and the fairness of those decisions, gambling and our intuitive understanding of chance, the likelihood of the extremely rare, the existence of true randomness and how computers have helped shape modern thinking about probabilities… An insightful chapter is ‘Chance or Necessity?’ The question is very, very old (determinism versus chaos), and the answer is not clear even today. The author describes the problem beautifully: ‘Is random outcome completely determined, and random only by virtue of our ignorance of the most minute contributing factors?’ Einstein grappled with this conundrum until his death and never ceased to combat the idea that God could conceivably throw dice… Whether well-educated in mathematics or not, people have always been fascinated by randomness and intrigued by the fundamental question of the real nature of randomness, of how you can tell randomness from something that is not. -- J. A. Rial * American Scientist *The great strength of this book is the way it uses history and even prehistory of probability to chart its present territory and cast light on its core point of contention: does true randomness exist in nature, or is it only a psychological artefact?… Bennett’s text…is like a café conversation between likable cognoscenti…nothing could more provoke and excite the reader. -- Simon Ings * New Scientist *In this book, Bennett seeks to account for the centuries-long lapse between early uses of chance in decision making and the more technical studies of probability first undertaken in the seventeenth century. At the same time, she explores the confusions and misunderstandings about probability that persist today. She argues that the notion of randomness played a crucial role in inhibiting conceptual progress in probability and that it also accounts for present-day struggles to come to terms with the subject… Bennett’s book is written in a lucid, engaging style and provides an entertaining introduction to some questions in probability. -- Patti Wilger Hunter * Isis *[A] sharp analysis of the way we assess probability in everyday life. -- Robert Winder * New Statesman & Society *Randomness, by mathematician Deborah J. Bennett, was obviously a labor of love. The result is an interesting book that combines a well-researched, anecdotally presented survey of the history of chance, probability and randomness along with some elementary instruction in probability… It includes a wide-ranging and rich bibliography that reflects the passion of the author for the subject. Anybody interested in gaming, random numbers, the Monte Carlo method and so on will find nice anecdotal descriptions of these topics, together with detailed notes and references to the bibliography for more detailed study. It is a good book to have. -- Stephen Gasiorowicz * Physics Today *In 1996 Charles Hailey and David Helfand reported their calculations of the odds of a commercial airliner being struck by a meteor, in response to speculation about TWA flight 800… They conclude that, in over 30 years of air travel, the probability that a commercial flight would have been hit by a meteor big enough to crash it is 1 in 10. This bit of probability trivia is an indication of human beings continuous struggle to understand probability and chance through the ages, and Deborah Bennett captures the fascination with numbers in this pocket-sized volume. The book is filled with…gems. * Skeptic *This volume is exceptionally readable. It takes away much of the mystery of probability while adding to our sense of wonder. * Wordtrade *The fact that randomness, agency, and holiness can readily displace each other in phenomenological explanations of human action is the central concern that might draw students of consciousness to Bennett’s book. Bennett does an excellent job, explaining and drawing out the major questions that swirl around the randomness–agency–holiness issue. -- T. W. Draper * Journal of Consciousness Studies *[This book] examines randomness and several other notions that were critical to the historical development of probabilistic thinking and that also play an important role in any individual’s understanding of the laws of chance. [It] addresses why, from ancient times to today, people have resorted to chance in making decisions; whether a decision made by random choice is a fair decision; how to figure the odds; what role gambling has played in understanding chance; whether extremely rare events are likely in the long run; why some societies and individuals reject randomness; whether true randomness exists; the view of randomness as uncertainty; why even experts disagree about the many meanings of randomness; and why probability is so counterintuitive. * Journal of Economic Literature *Mathematics is its own language, and sometimes it doesn’t translate readily into other human tongues. But Bennett is brilliantly bilingual, well able to put mathematical concepts into clear, expressive English. Her topic is intrinsically fascinating, for who has not felt buffeted by random events, and who has not sought to see when the wheel of fortune may turn up good luck?… More than an intriguing exploration of a peculiarly fascinating part of mathematics, its coverage, ranging from ancient games of chance to modern probability mind-games, makes it comprehensive as well as compulsively readable. -- Patricia Monaghan * Booklist *A clear and detailed examination of the role of pure chance, with fascinating historical asides. * Kirkus Reviews *A careful and well-written treatment of an intriguing subject. -- Donald Goldsmith, author of The Ultimate EinsteinRandomness tells us about chance by recalling the real history of probability and solving many of its engaging puzzles. Beginners will find themselves welcomed and well led. -- Frederick Mosteller, Harvard UniversityRandomness explains probability and odds in an accessible way. This book puts risk and chance into perspective for the airline passenger and the lottery player alike. -- Henry Petroski, author of Invention by Design: How Engineers Get from Thought to ThingTable of Contents* Chance Encounters * Why Resort to Chance? * When the Gods Played Dice * Figuring the Odds * Thought Games for Gamblers * Chance or Necessity? * Order in Apparent Chaos * Wanted: Random Numbers * Randomness as Uncertainty * Paradoxes in Probability * Notes * Bibliography * Index
£24.26
ISTE Ltd and John Wiley & Sons Inc Theory and Statistical Applications of Stochastic
Book SynopsisThis book is concerned with the theory of stochastic processes and the theoretical aspects of statistics for stochastic processes. It combines classic topics such as construction of stochastic processes, associated filtrations, processes with independent increments, Gaussian processes, martingales, Markov properties, continuity and related properties of trajectories with contemporary subjects: integration with respect to Gaussian processes, Itȏ integration, stochastic analysis, stochastic differential equations, fractional Brownian motion and parameter estimation in diffusion models. Table of ContentsPreface xi Introduction xiii Part 1 Theory of Stochastic Processes 1 Chapter 1 Stochastic Processes General Properties. Trajectories, Finite-dimensional Distributions 3 1.1 Definition of a stochastic process 3 1.2 Trajectories of a stochastic process Some examples of stochastic processes 5 1.2.1 Definition of trajectory and some examples 5 1.2.2 Trajectory of a stochastic process as a random element.8 1.3 Finite-dimensional distributions of stochastic processes: consistency conditions.10 1.3.1 Definition and properties of finite-dimensional distributions 10 1.3.2 Consistency conditions.11 1.3.3 Cylinder sets and generated σ-algebra 13 1.3.4 Kolmogorov theorem on the construction of a stochastic process by the family of probability distributions 15 1.4 Properties of σ-algebra generated by cylinder sets. The notion of σ-algebra generated by a stochastic process 19 Chapter 2 Stochastic Processes with Independent Increments 21 2.1 Existence of processes with independent increments in terms of incremental characteristic functions 21 2.2 Wiener process 24 2.2.1 One-dimensional Wiener process 24 2.2.2 Independent stochastic processes Multidimensional Wiener process 24 2.3 Poisson process 27 2.3.1 Poisson process defined via the existence theorem 27 2.3.2 Poisson process defined via the distributions of the increments 28 2.3.3 Poisson process as a renewal process 30 2.4 Compound Poisson process 33 2.5 Lévy processes 34 2.5.1 Wiener process with a drift 36 2.5.2 Compound Poisson process as a Lévy process 36 2.5.3 Sum of a Wiener process with a drift and a Poisson process 36 2.5.4 Gamma process 37 2.5.5 Stable Lévy motion37 2.5.6 Stable Lévy subordinator with stability parameter α ∈ (0, 1) 38 Chapter 3 Gaussian Processes Integration with Respect to Gaussian Processes 39 3.1 Gaussian vectors 39 3.2 Theorem of Gaussian representation (theorem on normal correlation) 42 3.3 Gaussian processes. 44 3.4 Examples of Gaussian processes 46 3.4.1 Wiener process as an example of a Gaussian process 46 3.4.2 Fractional Brownian motion.48 3.4.3 Sub-fractional and bi-fractional Brownian motion 50 3.4.4 Brownian bridge 50 3.4.5 Ornstein–Uhlenbeck process 51 3.5 Integration of non-random functions with respect to Gaussian processes 52 3.5.1 General approach 52 3.5.2 Integration of non-random functions with respect to the Wiener process 54 3.5.3 Integration w.r.t the fractional Brownian motion 57 3.6 Two-sided Wiener process and fractional Brownian motion: Mandelbrot–van Ness representation of fractional Brownian motion 60 3.7 Representation of fractional Brownian motion as the Wiener integral on the compact integral 63 Chapter 4 Construction, Properties and Some Functionals of the Wiener Process and Fractional Brownian Motion 67 4.1 Construction of a Wiener process on the interval [0, 1] 67 4.2 Construction of a Wiener process on R+ 72 4.3 Nowhere differentiability of the trajectories of a Wiener process 74 4.4 Power variation of the Wiener process and of the fractional Brownian motion77 4.4.1 Ergodic theorem for power variations 77 4.5 Self-similar stochastic processes 79 4.5.1 Definition of self-similarity and some examples 79 4.5.2 Power variations of self-similar processes on finite intervals.80 Chapter 5 Martingales and Related Processes 85 5.1 Notion of stochastic basis with filtration 85 5.2 Notion of (sub-, super-) martingale: elementary properties 86 5.3 Examples of (sub-, super-) martingales 87 5.4 Markov moments and stopping times 90 5.5 Martingales and related processes with discrete time 96 5.5.1 Upcrossings of the interval and existence of the limit of submartingale 96 5.5.2 Examples of martingales having a limit and of uniformly and non-uniformly integrable martingales 102 5.5.3 Lévy convergence theorem 104 5.5.4 Optional stopping 105 5.5.5 Maximal inequalities for (sub-, super-) martingales 108 5.5.6 Doob decomposition for the integrable processes with discrete time 111 5.5.7 Quadratic variation and quadratic characteristics: Burkholder–Davis–Gundy inequalities 113 5.5.8 Change of probability measure and Girsanov theorem for discrete-time processes 116 5.5.9 Strong law of large numbers for martingales with discrete time 120 5.6 Lévy martingale stopped 126 5.7 Martingales with continuous time 127 Chapter 6 Regularity of Trajectories of Stochastic Processes 131 6.1 Continuity in probability and in L2(Ω,F, P) 131 6.2 Modification of stochastic processes: stochastically equivalent and indistinguishable processes 133 6.3 Separable stochastic processes: existence of separable modification 135 6.4 Conditions of D-regularity and absence of the discontinuities of the second kind for stochastic processes 138 6.4.1 Skorokhod conditions of D-regularity in terms of three-dimensional distributions 138 6.4.2 Conditions of absence of the discontinuities of the second kind formulated in terms of conditional probabilities of large increments 144 6.5 Conditions of continuity of trajectories of stochastic processes 148 6.5.1 Kolmogorov conditions of continuity in terms of two-dimensional distributions 148 6.5.2 Hölder continuity of stochastic processes: a sufficient condition 152 6.5.3 Conditions of continuity in terms of conditional probabilities 154 Chapter 7 Markov and Diffusion Processes 157 7.1 Markov property 157 7.2 Examples of Markov processes 163 7.2.1 Discrete-time Markov chain 163 7.2.2 Continuous-time Markov chain 165 7.2.3 Process with independent increments 168 7.3 Semigroup resolvent operator and generator related to the homogeneous Markov process 168 7.3.1 Semigroup related to Markov process 168 7.3.2 Resolvent operator and resolvent equation 169 7.3.3 Generator of a semigroup.171 7.4 Definition and basic properties of diffusion process 175 7.5 Homogeneous diffusion process Wiener process as a diffusion process 179 7.6 Kolmogorov equations for diffusions 181 Chapter 8 Stochastic Integration 187 8.1 Motivation..187 8.2 Definition of Itô integral 189 8.2.1 Itô integral of Wiener process 195 8.3 Continuity of Itô integral 197 8.4 Extended Itô integral 199 8.5 Itô processes and Itô formula 203 8.6 Multivariate stochastic calculus 212 8.7 Maximal inequalities for Itô martingales 215 8.7.1 Strong law of large numbers for Itô local martingales 218 8.8 Lévy martingale characterization of Wiener process 220 8.9 Girsanov theorem 223 8.10 Itô representation 228 Chapter 9 Stochastic Differential Equations.233 9.1 Definition, solvability conditions, examples 233 9.1.1 Existence and uniqueness of solution 234 9.1.2 Some special stochastic differential equations 238 9.2 Properties of solutions to stochastic differential equations 241 9.3 Continuous dependence of solutions on coefficients 245 9.4 Weak solutions to stochastic differential equations. 247 9.5 Solutions to SDEs as diffusion processe 249 9.6 Viability, comparison and positivity of solutions to stochastic differential equations 252 9.6.1 Comparison theorem for one-dimensional projections of stochastic differential equations 257 9.6.2 Non-negativity of solutions to stochastic differential equations 258 9.7 Feynman–Kac formula 258 9.8 Diffusion model of financial markets 260 9.8.1 Admissible portfolios, arbitrage and equivalent martingale measure 263 9.8.2 Contingent claims, pricing and hedging 266 Part 2 Statistics of Stochastic Processes 271 Chapter 10 Parameter Estimation 273 10.1 Drift and diffusion parameter estimation in the linear regression model with discrete time 273 10.1.1 Drift estimation in the linear regression model with discrete time in the case when the initial value is known 274 10.1.2 Drift estimation in the case when the initial value is unknown 277 10.2 Estimation of the diffusion coefficient in a linear regression model with discrete time 277 10.3 Drift and diffusion parameter estimation in the linear model with continuous time and the Wiener noise 278 10.3.1 Drift parameter estimation 279 10.3.2 Diffusion parameter estimation 280 10.4 Parameter estimation in linear models with fractional Brownian motion 281 10.4.1 Estimation of Hurst index 281 10.4.2 Estimation of the diffusion parameter 283 10.5 Drift parameter estimation 284 10.6 Drift parameter estimation in the simplest autoregressive model 285 10.7 Drift parameters estimation in the homogeneous diffusion model 289 Chapter 11 Filtering Problem Kalman-Bucy Filter 293 11.1 General setting 293 11.2 Auxiliary properties of the non-observable process 294 11.3 What is an optimal filter 295 11.4 Representation of an optimal filter via an integral equation with respect to an observable process 296 11.5 Integral Wiener-Hopf equation 299 Appendices 311 Appendix 1 313 Appendix 2 329 Bibliography 363 Index 369
£125.06
ISTE Ltd and John Wiley & Sons Inc Discrete Time Branching Processes in Random
Book SynopsisBranching processes are stochastic processes which represent the reproduction of particles, such as individuals within a population, and thereby model demographic stochasticity. In branching processes in random environment (BPREs), additional environmental stochasticity is incorporated, meaning that the conditions of reproduction may vary in a random fashion from one generation to the next. This book offers an introduction to the basics of BPREs and then presents the cases of critical and subcritical processes in detail, the latter dividing into weakly, intermediate, and strongly subcritical regimes.Table of Contents1. Branching Processes in Varying Environment. 2. Branching Processes in Random Environment. 3. Large Deviations for BPREs. 4. Properties of Random Walks. 5. Critical BPREs: the Annealed Approach. 6. Critical BPREs: the Quenched Approach. 7. Weakly Subcritical BPREs. 8. Intermediate Subcritical BPREs. 9. Strongly Subcritical BPREs. 10. Multi-type BPREs.
£125.06
ISTE Ltd and John Wiley & Sons Inc Introduction to Stochastic Processes and
Book SynopsisMastering chance has, for a long time, been a preoccupation of mathematical research. Today, we possess a predictive approach to the evolution of systems based on the theory of probabilities. Even so, uncovering this subject is sometimes complex, because it necessitates a good knowledge of the underlying mathematics. This book offers an introduction to the processes linked to the fluctuations in chance and the use of numerical methods to approach solutions that are difficult to obtain through an analytical approach. It takes classic examples of inventory and queueing management, and addresses more diverse subjects such as equipment reliability, genetics, population dynamics, physics and even market finance. It is addressed to those at Master�s level, at university, engineering school or management school, but also to an audience of those in continuing education, in order that they may discover the vast field of decision support.Table of ContentsPart 1. Basic Mathematical Concepts 1. Basic Reminders of Probability. 2. Probabilistic Models. 3. Inventory Management. Part 2. Stochastic Processes 4. Markov Chains. 5. Markov Processes. 6. Queueing Systems. 7. Various Applications. Part 3. Simulation 8. Generator Programs. 9. Principles of Simulation. 10. Simulation of Inventory Management. 11. Simulation of a Queueing Process. 12. Optimization and Simulation.
£125.06
