Statistics and Probability Catalogue 2016

Statistics and Probability 2016

cambridge.org/statistics2016

Welcome to the Statistics and Probability books catalogue 2016.

Here you will find new and forthcoming titles, representing cutting-edge research and practical applications from renowned authors. Our highlights this year include Truth or Truthiness by Howard Wainer, David Stirzaker’s Cambridge Dictionary of Probability and Its Applications, Causal Inference for Statistics, Social, and Biomedical Sciences by Guido Imbens and Donald Rubin, and Computer-Age Statistical Inference by Bradley Efron and Trevor Hastie.

Our publications are available in a variety of formats, including ebooks and print, as well as online collections for institutional purchase via ebooks.cambridge.org.

We also publish a range of Statistics and Probability journals and from 2016 we have added two journals from the Applied Probability Trust: Journal of Applied Probability and Advances in Applied Probability. You can recommend our books, journals, and online collections to your librarian by filling out the form at the back of this catalogue.

To see more book listings and product information, preview extracts and reviews, and find out which conferences we are attending, visit us online at www.cambridge.org/ Statistics2016.

You can also keep up to date with the latest news and author views from our academic blog at www.cambridgeblog.org.

We hope that you enjoy reading about our latest publications. If you have questions, you can find a list of useful contacts at the back of this catalogue.

If you are interested in publishing your book with Cambridge, please contact Diana Gillooly, dgillooly@cambridge.org.

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THE CAMBRID GE DICTIONARY OF

ÐMore than 2,900 terms, grouped into 2,200 entries – from Abelian sandpile to Z-transform

Stirzaker. 9781107075160 PPC. C M Y K

ÐWell-known author with a reputation for quality ÐRich cross-referencing reveals connections between concepts ÐIdentification of ‘aliases’ reduces divergent terminology ÐReferences to the wider literature show readers ‘what’s next?’ ÐJudiciously illustrated making the more complicated concepts

Features 1 Statistical Theory and Methods 3 Computational Statistics, Machine Learning and Information Science 7 Methods for the Life Sciences 11 Methods for the Physical Sciences 12 Methods for the Social Sciences 15 Methods for Economics, Finance and Insurance 18 Probability 24 Applied Probability and Stochastic Networks 28 Optimization, OR and Risk 32 Also of Interest 36 Information on related journals Inside back cover easier to follow.

T H E C A M B R I D G E D I C T I O NA RY O F

The dictionary is reliable, stable, concise, and cohesive. Each entry provides a rigorous definition, a sketch of the context, and a reference pointing the reader to the wider literature. As the only dictionary on the market, this will be a guiding reference for all those working in, or learning, probability together with its applications.

Probability and Its Applications

Contents

STIRZAKER

PROBABILITY COMES OF AGE with this, the first dictionary of probability and its applications in English, which supplies a guide to the concepts and vocabulary of this rapidly expanding field. Besides the basic theory of probability and random processes, applications covered here include financial and insurance mathematics, operations research (including queueing, reliability, and inventories), decision and game theory, optimization, time series, networks, and communication theory, as well as classic problems and paradoxes.

Probability and Its Applications

see page 1

DAVID STIRZAKER

cover illustration: Vasya Kobelev / Shutterstock.com

“Causal Inference sets a high new standard for discussions of the theoretical and practical issues in the design of studies for assessing the effects of causes–from an array of methods for using covariates in real studies to dealing with many subtle aspects of non-compliance with assigned treatments. The book includes many examples using real data that arose from the authors’ extensive research portfolios.

These examples help to clarify and explain many important concepts and practical issues. It is a book that both methodologists and practitioners from many fields will find both illuminating and suggestive of further research. It is a professional tour de force, and a welcomed addition to the growing (and often confusing) literature on causation in artificial intelligence, philosophy, mathematics, and statistics.”

– Dr. Paul W. Holland, Emeritus, Educational Testing Service

“This book will revolutionize how applied statistics is taught in statistics and the social and biomedical sciences. The authors present a unified vision of causal inference that covers both experimental and observational data. They do a masterful job of communicating some of the deepest, and oldest, issues in statistics to readers with disparate backgrounds. They closely connect theoretical concepts with applied concerns, and they honestly and clearly discuss the identifying assumptions of the methods presented. Too many books on statistical methods present a menagerie of disconnected methods and pay little attention to the scientific plausibility of the assumptions that are made for mathematical convenience, instead of for verisimilitude. This book is different. It will be widely read, and it will change the way statistics is practiced.”

–Jasjeet S. Sekhon, Robson Professor of Political Science and Statistics, University of California, Berkeley “By putting the potential outcome framework at the center of our understanding of causality, Imbens and Rubin have ushered in a fundamental transformation of empirical work in economics. This book, at once transparent and deep, will be both a fantastic introduction to fundamental principles and a practical resource for students and practitioners. It will be required reading for any class I teach.”

– Esther Duflo, Professor of Poverty Alleviation and Development Economics, Massachusetts Institute of Technology “This book will be the ‘Bible’ for anyone interested in the statistical approach to causal inference associated with Donald Rubin and his colleagues, including Guido Imbens. Together, they have systematized the early insights of Fisher and Neyman and have then vastly developed and transformed them. In the process they have created a theory of practical experimentation whose internal consistency is mind-boggling, as is its sensitivity to assumptions and its elaboration of the key ‘potential outcomes’ framework. The authors’ exposition of random assignment experiments has breadth and clarity of coverage, as do their chapters on observational studies that can be readily conceptualized within an experimental framework. Never have experimental principles been better warranted intellectually or better translated into statistical practice. The book is a must-read for anyone claiming methodological competence in all sciences that rely on experimentation.” – Thomas D. Cook, Joan and Sarepta Harrison Chair of Ethics and Justice, andn Professor of Sociology, Psychology, and Education and Social Policy, Northwestern University “A comprehensive and remarkably clear overview of randomized experiments and observational designs with as-good-as-random assignment that is sure to become the standard reference in the field.”

– David Card, Class of 1950 Professor of Economics, University of California, Berkeley “This book offers a definitive treatment of causality using the potential outcomes approach. Both theoreticians and applied researchers will find this an indispensable volume for guidance and reference.”

– Hal Varian, Chief Economist, Google, and Emeritus Professor, University of California, Berkeley

CAUSAL INFERENCE FOR STATISTICS, SOCIAL, AND BIOMEDICAL SCIENCES

“In this wonderful and important book, Imbens and Rubin give a lucid account of the potential outcomes perspective on causality.

This perspective sensibly treats all causal questions as questions about a hidden variable, indeed the ultimate hidden variable, ‘What would have happened if things were different?’ They make this perspective mathematically precise, show when and to what degree it succeeds, and discuss how to apply it to both experimental and observational data. This book is a must-read for natural scientists, social scientists, and all other practitioners who seek new hypotheses and new truths in their complex data.”

–David Blei, Professor of Computer Science, Columbia University

IMBENS • RUBIN

Advance Praise for Causal Inference for Statistics, Social, and Biomedical Sciences

see page 1

CAUSAL INFERENCE

see page 2

FOR

STATISTICS, SOCIAL, AND

BIOMEDICAL SCIENCES AN INTRODUCTION

GUIDO W. IMBENS DONALD B. RUBIN

Cover design by Alice Soloway

see page 7

Featured authors Howard Wainer Author of Truth or Truthiness: Separating Fact from Fiction by Learning to Think Like a Data Scientist The past decade has seen a widening of the dichotomy in public discourse between those whose views are based on evidence and those who rely primarily on deeply held beliefs. The fact that evidence-based predictions come to pass much more often, has unsurprisingly, had little effect on the views of members of the other camp. I wrote this book to provide guidance to those whose orientation is not yet settled. It contains real-life examples of how evidence helps us make demonstrably better decisions. It also contains explanations of some of the tools used by modern data scientists, shorn of the technical characteristics that might put them out of reach of an intelligent but lay audience. I also tried to convey how much fun it is to do science with evidence.

Carola-Bibiane Schönlieb, Reader in Applied and Computational Analysis at University of Cambridge

Author of Partial Differential Equation Methods for Image Inpainting I wanted to write a book on partial differential equation (PDE) techniques for image inpainting, alias for digital image restoration, because I felt the rich and vast literature on this fascinating topic, scattered across many research papers in applied mathematics, deserves an embracing exposition. Doing this now is extremely timely in my opinion. No such exposition existed prior to this publication, and we are currently experiencing a turing point in this field where classical methods featured in this book are revisited and enriched by new concepts and ideas.

W. John Braun, Univeristy of Western Ontario Duncan J. Murdoch, Univeristy of Western Ontario Authors of A First Course in Statistical Programming with R My co-author, Duncan Murdoch, and I wanted to write a book that would fill what we saw as a gap in the statistical and actuarial science curriculum: statistically oriented computer programming. Using the scripting language R as a vehicle to convey the essential programming and debugging concepts was a convenient way of achieving our goal.

Visit www.cambridge.org/authorhub for a range of step-by-step guides for authors

Features

Features Truth or Truthiness Distinguishing Fact from Fiction

by Learning to Think Like a Data Scientist Howard Wainer National Board of Medical Examiners, Philadelphia, Pennsylvania

Escaping the clutches of truthiness begins with one simple question: ‘what is the evidence?’ Howard Wainer shows how the sceptical mindset of a data scientist can expose truthiness, nonsense, and outright deception. He evaluates the evidence, or lack thereof, supporting claims in many fields, with special emphasis in education.

‘This book is like the proverbial bag of potato chips. It’s impossible to stop reading after just one of its fun and thought-provoking examples of statistical reasoning.’

Andrew Gelman, Columbia University, New York

Contents: Part I. Thinking Like a Data Scientist; Part II. Communicating Like a Data Scientist; Part III. Applying the Tools of Data Science to Education.

2015 228 x 152 mm 232pp 52 b/w illus. 9 colour illus. 12 tables 978-1-107-13057-9 Hardback £19.99 / US$29.99 For all formats available, see

www.cambridge.org/9781107130579

1

The Cambridge Dictionary of Probability and its Applications David Stirzaker St John’s College, Oxford

This book, the first dictionary of probability and its applications in English, guides the reader through the concepts and vocabulary of this rapidly expanding field. As the only dictionary on the market, it will be a guiding reference for all those working and studying in this area.

‘To construct a dictionary about such an enormous field is a daunting task, and David Stirzaker deserves high praise, first for even attempting to do so, and second for the success he has achieved. A dictionary’s usefulness depends on its organisation as well as on the quality of the individual entries, and this book’s structure is simple and logical: two initial pages list the abbreviations and symbols, then the main body of 3000-odd entries with easy-to-use cross-referencing, ending with an Appendix of probability distributions….I shall be delighted to possess this authoritative tome. It will sit alongside Abramowitz and Steguns’ Handbook of Mathematical Functions, as a reliable source of enlightenment.’

John Haigh, University of Sussex

PROSE Award for Mathematics 2016 – Honourable mention 2015 246 x 189 mm 426pp 80 b/w illus.

978-1-107-07516-0 Hardback £120.00 / US$195.00 For all formats available, see

www.cambridge.org/9781107075160

eBooks available at www.cambridge.org/ebookstore

2

Features Causal Inference for Statistics, Social, and Biomedical Sciences

Data Management Essentials Using SAS and JMP

An Introduction Guido W. Imbens

Julie M. Kezik Yale University School of Public Health

Stanford University, California

and Melissa E. Hill

and Donald B. Rubin

CD3 Inc.

Harvard University, Massachusetts

This book is designed for the first time or occasional SAS user who needs immediate guidance in navigating, exploring, visualizing, cleaning and reporting on data. It teaches the basic SAS skills essential to data management, including practical exercises with solutions. No formal or informal training is required.

This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.

‘This book offers a definitive treatment of causality using the potential outcomes approach. Both theoreticians and applied researchers will find this an indispensable volume for guidance and reference.’

Hal Varian, Chief Economist, Google, and Emeritus Professor, University of California, Berkeley

Contents: Part I. Introduction; Part II. Classical Randomized Experiments; Part III. Regular Assignment Mechanisms: Design; Part IV. Regular Assignment Mechanisms: Analysis; Part V. Regular Assignment Mechanisms: Supplementary Analyses; Part VI. Regular Assignment Mechanisms with Noncompliance: Analysis; Part VII. Conclusion.

PROSE Award for Textbook, Social Sciences 2016 – Winner 2015 253 x 177 mm 644pp 18 b/w illus. 97 tables 978-0-521-88588-1 Hardback £40.00 / US$60.00 For all formats available, see

www.cambridge.org/9780521885881

‘The authors have created a very readable and gentle introduction to SAS programming and its working environment – Enterprise Guide. The text provides a valuable overview of “navigating” in a SAS windowing environment and before moving quickly into core procedures. Overall a very valuable introduction to basic SAS programming for the beginning data analyst.’

Glenn Gamst, Psychology Department, University of La Verne 2016 234 x 177 mm 200pp 74 b/w illus. 4 tables 31 exercises 978-1-107-11456-2 Hardback c. £63.00 / c. US$108.00 978-1-107-53503-9 Paperback c. £24.99 / c. US$34.99 Publication April 2016 For all formats available, see

www.cambridge.org/9781107114562

Statistical Theory and Methods

Statistical Theory and Methods

3

Mathematical Foundations of Infinite-Dimensional Statistical Models Evarist Giné University of Connecticut

Confidence, Likelihood, Probability Statistical Inference with Confidence Distributions Tore Schweder and Nils Lid Hjort Universitetet i Oslo

The baby without the bathwater? This lively book lays out a methodology of confidence distributions and puts them through their paces with a generous mixture of theory, illustrations, applications and exercises – suitable for statisticians at all levels of experience, as well as for data-oriented scientists.

The methodology yields posterior distributions for parameters, but without the Bayesian ingredients.

‘This book presents a detailed and wide-ranging account of an approach to inference that moves the discipline towards increased cohesion, avoiding the artificial distinction between testing and estimation. Innovative and thorough, it is sure to have an impact both in the foundations of inference and in a wide range of practical applications of inference.’

and Richard Nickl University of Cambridge

High-dimensional and nonparametric statistical models are ubiquitous in modern data science. This book develops a mathematically coherent and objective approach to statistical inference in such models, with a focus on function estimation problems arising from random samples (density estimation) or from Gaussian regression/signal in white noise problems.

‘Finally – a book that goes all the way in the mathematics of nonparametric statistics. It is reasonably selfcontained, despite its depth and breadth, including accessible overviews of the necessary analysis and approximation theory.’

Aad van der Vaart, Universiteit Leiden Cambridge Series in Statistical and Probabilistic Mathematics, 40

2015 253 x 177 mm 720pp 978-1-107-04316-9 Hardback £59.99 / US$99.99 For all formats available, see

www.cambridge.org/9781107043169

Nancy Reid, University Professor of Statistical Sciences, University of Toronto Cambridge Series in Statistical and Probabilistic Mathematics, 41

2016 253 x 177 mm 544pp 147 b/w illus. 17 tables 100 exercises 978-0-521-86160-1 Hardback £52.99 / US$84.99 Publication February 2016 For all formats available, see

www.cambridge.org/9780521861601

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4

Statistical Theory and Methods Core Statistics Simon N. Wood University of Bath

Core Statistics is a compact starter course on the fundamentals of inference for parametric statistical models, including both theory and practical numerical computation. It delivers the theory and tools that a beginning graduate student, or any quantitative scientist, needs to make informed use of powerful statistical methods.

‘The author keeps this book concise by focusing entirely on topics that are most relevant for scientific modeling via maximum likelihood and Bayesian inference. This makes it an ideal text and handy reference for any mathliterate scientist who wants to learn how to build sophisticated parametric models and fit them to data using modern computational approaches.

I will be recommending this wellwritten book to my collaborators.’

Murali Haran, Pennsylvania State University Institute of Mathematical Statistics Textbooks, 6

2015 228 x 152 mm 258pp 43 b/w illus. 2 tables 51 exercises 978-1-107-07105-6 Hardback £60.00 / US$99.00 978-1-107-41504-1 Paperback £24.99 / US$39.99 For all formats available, see

www.cambridge.org/9781107071056

Computer-Age Statistical Inference Bradley Efron Stanford University, California

and Trevor Hastie Stanford University, California

This landmark book lays out the interplay between methodology and inference from the early computer age to the state of the art. After reviewing classical inference and computation, it tackles the present, including false discovery rates, automatic model building (Lasso and LARS), objective Bayes inference, machine learning methods, and inference after model selection.

2016 228 x 152 mm 450pp 978-1-107-14989-2 Hardback c. £45.99 / c. US$74.99 Publication August 2016 For all formats available, see

www.cambridge.org/9781107149892

Modeling Count Data Joseph M. Hilbe Arizona State University

Written for researchers with little or no background in advanced statistics, this book provides guidelines and fully worked examples of how to select, construct, interpret and evaluate the full range of count models. Stata, R, and SAS code enable readers in a variety of disciplines to adapt models for their own purposes.

‘This is a first-rate introductory book for modeling count data, a key challenge in applied statistics.

Hilbe’s experience and affability shine in the text. His careful emphasis on establishing the defensibility of models, for example, in the face of

Statistical Theory and Methods overdispersion, will greatly benefit the beginning statistician. His clear informal explanations of important and complicated statistical principles are invaluable.’

Andrew Robinson, University of Melbourne

PROSE Award for Mathematics 2015 – Honourable mention 2014 234 x 177 mm 300pp 10 b/w illus. 81 tables 978-1-107-02833-3 Hardback £69.99 / US$115.00 978-1-107-61125-2 Paperback £27.99 / US$39.99 For all formats available, see

www.cambridge.org/9781107028333

Counterfactuals and Causal Inference Methods and Principles for Social Research Second edition Stephen L. Morgan

5

introduction to the topic, and a fine place to begin learning causal inference.’

Tyler J. VanderWeele, Harvard University, Massachusetts

Contents: Part I. Causality and Empirical Research in the Social Sciences; Part II. Counterfactuals, Potential Outcomes, and Causal Graphs; Part III. Estimating Causal Effects by Conditioning on Observed Variables to Block Backdoor Paths; Part IV. Estimating Causal Effects When Backdoor Conditioning Is Ineffective; Part V. Estimation When Causal Effects Are Not Point Identified

by Observables; Part VI. Conclusions.

Analytical Methods for Social Research

2014 253 x 177 mm 524pp 64 b/w illus.

978-1-107-06507-9 Hardback £65.00 / US$99.00 978-1-107-69416-3 Paperback £24.99 / US$39.99 For all formats available, see

www.cambridge.org/9781107065079

The Johns Hopkins University

and Christopher Winship Harvard University, Massachusetts

Cause-and-effect questions are the motivation for most research in the social, demographic, and health sciences. The counterfactual approach to causal analysis represents a unified framework for the prosecution of these questions. This second edition aims to convince more social scientists to take this approach when analyzing these core empirical questions.

‘The use of counterfactuals for causal inference has brought clarity to our reasoning about causality. And this second edition by Morgan and Winship will bring clarity to anyone trying to learn about the field. It is an excellent

Visit our website at www.cambridge.org/academic

6

Statistical Theory and Methods Large Sample Covariance Matrices and High-Dimensional Data Analysis Jianfeng Yao The University of Hong Kong

Shurong Zheng Northeast Normal University, China

and Zhidong Bai Northeast Normal University, China

High-dimensional statistical methods are at the heart of the new era of big data analytics. This book, written by leading experts, is highly recommended for anyone who wants to make serious use of these modern statistical tools.

‘This is the first book which treats systematic corrections to the classical multivariate statistical procedures so that the resultant procedures can be used for high-dimensional data.

The corrections have been done by employing asymptotic tools based on the theory of random matrices.’

Yasunori Fujikoshi, Hiroshima University, Japan Cambridge Series in Statistical and Probabilistic Mathematics, 39

2015 253 x 177 mm 322pp 80 b/w illus. 30 tables 978-1-107-06517-8 Hardback £52.00 / US$85.00 For all formats available, see

www.cambridge.org/9781107065178

Nonparametric Estimation under Shape Constraints Estimators, Algorithms and Asymptotics Piet Groeneboom Technische Universiteit Delft, The Netherlands

and Geurt Jongbloed Technische Universiteit Delft, The Netherlands

This book treats the latest developments in the theory of order-restricted inference, with special attention to nonparametric methods and algorithmic aspects. Each chapter ends with a set of exercises of varying difficulty. The theory is illustrated with the analysis of real-life data, which are mostly medical in nature.

‘Shape constraints arise naturally in many statistical applications and are becoming increasingly popular as a means of combining the best of the parametric and nonparametric worlds.

This book, written by two experts in the field, gives a detailed treatment of many of their attractive features.

I have no doubt it will be a valuable resource for researchers, students, and others interested in learning about this fascinating area.’

Richard Samworth, University of Cambridge Cambridge Series in Statistical and Probabilistic Mathematics, 38

2014 253 x 177 mm 428pp 90 b/w illus. 20 tables 190 exercises 978-0-521-86401-5 Hardback £55.00 / US$85.00 For all formats available, see

www.cambridge.org/9780521864015

Computational Statistics, Machine Learning and Information Science

A First Course in Statistical Programming with R W. John Braun and Duncan J. Murdoch University of Western Ontario

Learn to program in R from the experts with this new, color edition of Braun and Murdoch’s bestselling textbook.

2016 246 x 189 mm 210pp 200 exercises 80 worked examples 978-1-107-57646-9 Paperback £24.99 / US$39.99

Machine Learning Refined Foundations, Algorithms, and Applications Jeremy Watt Northwestern University, Illinois

Reza Borhani Northwestern University, Illinois

and Aggelos Katsaggelos Northwestern University, Illinois

This is the only introduction you'll need to start programming in R, the open-source language that is free to download and lets you adapt the source code for your own requirements. Co-written by one of the R Core Development Team, and by an established R author, this book comes with real R code that complies with the standards of the language.

• Self-contained first course in statistical computing • Accessible to any student familiar with university-level calculus • The basics of R syntax and statistical graphics are explained, and elementary programming is discussed • Programming applications in simulation and optimization as well as numerical linear algebra are introduced • Many worked examples with notes on "understanding the code" • All examples are drawn from statistical applications • End-of-chapter review questions plus over 150 exercises; data sets and solutions all available on web

This book provides a fresh, intuitive approach to machine learning, detailing the fundamental concepts necessary for building projects and conducting research. With colour illustrations, practical real-world examples, and MATLAB-based exercises, it is an essential resource for students and an ideal reference for researchers and practitioners in the field.

braun and murdoch Introduction to Statistical Programming with R

Computational Statistics, Machine Learning and Information Science

7

a first course in

statistical programming with

designed by zoe naylor

Contents: Part I. The Basics; Part II. Automatic Feature Design; Part III. Tools for Large Scale Data; .

For all formats available see

2016 247 x 174 mm 300pp 135 colour illus. 3 tables 81 exercises 978-1-107-12352-6 Hardback c. £55.00 / c. US$90.00

www.cambridge.org/9781107576469

Publication June 2016 For all formats available, see

www.cambridge.org/9781107123526

eBooks available at www.cambridge.org/ebookstore

R

W. John Braun Duncan J. Murdoch

8

Computational Statistics, Machine Learning and Information Science Mining of Massive Datasets Second edition Jure Leskovec Stanford University, California

Anand Rajaraman Milliways Laboratories

and Jeffrey David Ullman Stanford University, California

Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond.

This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.

2014 247 x 174 mm 476pp 150 b/w illus. 210 exercises 978-1-107-07723-2 Hardback £45.00 / US$70.00 For all formats available, see

www.cambridge.org/9781107077232

Probabilistic Forecasting and Bayesian Data Assimilation Sebastian Reich Universität Potsdam, Germany and University of Reading

and Colin Cotter Imperial College London

This book focuses on the Bayesian approach to data assimilation, outlining the subject’s key ideas and concepts, and explaining how to implement specific data assimilation algorithms. It is an ideal introduction for graduate students in applied mathematics, computer science, engineering, geoscience and other emerging application areas.

Contents: Part I. Quantifying Uncertainty; Part II. Bayesian Data Assimilation.

2015 247 x 174 mm 308pp 70 b/w illus. 7 colour illus. 70 exercises 978-1-107-06939-8 Hardback £69.99 / US$115.00 978-1-107-66391-6 Paperback £34.99 / US$54.99 For all formats available, see

www.cambridge.org/9781107069398

Computational Statistics, Machine Learning and Information Science Sparse Image and Signal Processing Wavelets and Related Geometric Multiscale Analysis Second edition Jean-Luc Starck Centre d’etudes de Saclay, France

Fionn Murtagh Goldsmiths University of London and University of Derby

and Jalal Fadili Ecole Nationale Supérieure d’Ingénieurs de Caen, France

This thoroughly updated edition presents state-of-the-art sparse and multiscale image and signal processing with applications in astronomy, biology, physics, MRI, digital media, and forensics. New chapters and sections cover dictionary learning, 3-D data (data cubes), and geo-located data.

MATLAB® and IDL code are available.

Review of previous edition: ‘One of the main virtues of this book is the expert insight that the authors provide into several design and algorithmic choices that one can face when solving practical problems.

The authors give some guidance into understanding how sparsity helps in signal and image processing, what some benefits of overcomplete representations are, when to use isotropic wavelets for image processing, why morphological diversity can be helpful, and how to choose between analysis and synthesis priors for regularization in inverse problems.’

Michael B. Wakin, IEEE Signal Processing Magazine

9

Algorithms and Models for Network Data and Link Analysis François Fouss Université catholique de Louvain

Marco Saerens Université catholique de Louvain

and Masashi Shimbo Nara Institute of Science and Technology

Network data capture social and economic behavior in a form that can be analyzed using computational tools.

In this entry-level guide, algorithms for extracting information are derived in detail and summarized in pseudocode. This book is intended primarily for computer scientists, engineers, statisticians and physicists, but is accessible to social network scientists more broadly.

ALGORITHMS AND MODELS FOR

NETWORK DATA AND LINK ANALYSIS FR ANÇOIS FOUSS • MARCO SAERENS • MASASHI SHIMBO

2016 253 x 177 mm 525pp 14 b/w illus. 7 tables 978-1-107-12577-3 Hardback c. £50.00 / c. US$80.00 Publication May 2016 For all formats available, see

www.cambridge.org/9781107125773

2015 253 x 177 mm 428pp 194 b/w illus. 109 colour illus. 8 tables 978-1-107-08806-1 Hardback £54.99 / US$89.99 For all formats available, see

www.cambridge.org/9781107088061

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10

Computational Statistics, Machine Learning and Information Science Statistical Methods for Recommender Systems

Analytic Pattern Matching

Deepak K. Agarwal

From DNA to Twitter Philippe Jacquet

LinkedIn Corporation, California

and Bee-Chung Chen

Bell Laboratories

LinkedIn Corporation, California

and Wojciech Szpankowski

This book is for researchers and students in statistics, data mining, computer science, machine learning, marketing and also practitioners who implement recommender systems. It provides an in-depth discussion of challenges encountered in deploying real-life large-scale systems and state-of-the-art solutions in personalization, explore/ exploit, dimension reduction and multi-objective optimization.

Purdue University, Indiana

Contents: Part I. Introduction; Part II. Common Problem Settings; Part III. Advanced Topics.

2016 228 x 152 mm 288pp 66 b/w illus. 18 tables 978-1-107-03607-9 Hardback £34.99 / US$59.99 Publication February 2016 For all formats available, see

www.cambridge.org/9781107036079

How do you distinguish a cat from a dog by their DNA? Pattern matching problems like these play a key role in areas such as computer science, telecommunications and molecular biology. In this graduate-level book the authors present a unique probabilistic approach using analytic combinatorics and analytic information theory.

Contents: Part I. Analysis; Part II. Applications.

2015 247 x 174 mm 385pp 40 b/w illus. 110 exercises 978-0-521-87608-7 Hardback £45.00 / US$75.00 For all formats available, see

www.cambridge.org/9780521876087

Methods for the Life Science

Methods for the Life Sciences

Contents: Part I. Basics; Part II. Applications; Part III. Survival; Part IV. Epidemiology; Part V. Genetics; Part VI. Theory; .

A Biostatistics Toolbox for Data Analysis

2015 253 x 177 mm 578pp 146 b/w illus. 290 tables 978-1-107-11308-4 Hardback £39.99 / US$64.99

Steve Selvin

For all formats available, see

University of California, Berkeley

Selvin delivers a sophisticated package of statistical methods for advanced master’s (MPH) and PhD students in public health and epidemiology, involved in data analysis.

‘Professor Selvin is a master at making statistical procedures and their complex underpinnings accessible to students of all levels of expertise.

This book is a brilliant compendium of Professor Selvin’s tremendous understanding of the breadth and depth of biostatistical tools that he delivers to the reader with superb clarity. A broad range of salient statistical concepts are covered, pleasantly anchored with a brief history, described formally for the more initiated reader, and expertly illustrated with real-life data examples that are readily understood by the less mathematically inclined. Researchers from a myriad of scientific disciplines seeking masterful guidance about conducting their statistical data analysis will absolutely want this book at their fingertips.’

11

www.cambridge.org/9781107113084

Integrating Omics Data George Tseng University of Pittsburgh

Debashis Ghosh Colorado School of Public Health

and Xianghong Jasmine Zhou University of Southern California

This book provides comprehensive coverage of information integration of omics, experimental data, and databases. It introduces state-of-theart methods developed by leaders in the field to handle information integration problems of omics data.

Popular technologies include microarray, next-generation sequencing, mass spectrometry and proteomic assays.

2015 228 x 152 mm 488pp 147 b/w illus. 23 colour illus. 31 tables 978-1-107-06911-4 Hardback £80.00 / US$125.00 For all formats available, see

www.cambridge.org/9781107069114

Gary Shaw, Stanford University, California

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12

Methods for the Life Science / Methods for the Physical Sciences Cause and Correlation in Biology A User’s Guide to Path Analysis, Structural Equations and Causal Inference with R Second edition Bill Shipley Université de Sherbrooke, Canada

Written for biologists and students, this practical guide underlies the principle methods for analysing cause-effect relationships. Featuring extensive sections on the use of R statistical language to apply statistical methods to biological data, this completely revised new edition is a valuable resource for practising biologists.

Review of previous edition: ‘… the perfect introduction to SEM. This book can be used as the primary text in a SEM course given within any discipline, and can be used by scholars and researchers from any area of science.’

Structural Equation Modeling 2016 247 x 174 mm 320pp 113 b/w illus. 22 tables 978-1-107-44259-7 Paperback £39.99 / US$64.99 Publication May 2016 For all formats available, see

www.cambridge.org/9781107442597

Methods for the Physical Sciences Quantitative Methods of Data Analysis Douglas G. Martinson Columbia University, New York

This introduction focuses on demystifying methods for end-users. It explains conceptually why and when different methods work; it derives their underlying mathematics in elementary terms; and it examines their computational use, including wrinkles that arise in implementation. Each chapter ends with conceptual and theoretical exercises as well as computational ‘practicals’.

2016 247 x 174 mm 550pp 978-1-107-02976-7 Hardback c. £53.00 / c. US$85.00 Publication November 2016 For all formats available, see

www.cambridge.org/9781107029767

Methods for the Physical Sciences Value of Information in the Earth Sciences Integrating Spatial Modeling and Decision Analysis Jo Eidsvik Norwegian University of Science and Technology, Trondheim

Tapan Mukerji Stanford University, California

and Debarun Bhattacharjya IBM T. J. Watson Research Center, New York

This book presents a unified framework for assessing the value of potential data gathering schemes by integrating spatial modelling and decision analysis, with a focus on the Earth sciences.

Real datasets and MATLAB codes are provided online, making this an invaluable reference for students, researchers, and industry professionals.

2015 247 x 174 mm 396pp 137 b/w illus. 22 tables 978-1-107-04026-7 Hardback £89.99 / US$140.00 For all formats available, see

www.cambridge.org/9781107040267

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Textbook

Random Processes for Engineers Bruce Hajek University of Illinois, Urbana-Champaign

An engaging introduction to the critical tools needed to design and evaluate engineering systems that must operate reliably in uncertain environments. It includes over 100 worked examples and over 300 end-of-chapter problems, with worked solutions to half provided in the book and the remaining solutions available online for instructors.

‘A comprehensive exposition of random processes … Abstract concepts are nicely explained through many examples … The book will be very helpful for beginning graduate students who want a firm foundational understanding of random processes. It will also serve as a nice reference for the advanced reader.’

Anima Anandkumar, University of California, Irvine

Contents: 1. A selective review of basic probability; 2. Convergence of a sequence of random variables; 3. Random vectors and minimum mean squared error estimation; 4. Random processes; 5. Inference for Markov models; 6. Dynamics for countablestate Markov models; 7. Basic calculus of random processes; 8. Random processes in linear systems and spectral analysis; 9. Wiener filtering; 10. Martingales; 11. Appendix; 12. Solutions to even numbered problems.

2015 247 x 174 mm 432pp 130 b/w illus. 1 table 307 exercises 978-1-107-10012-1 Hardback £44.99 / US$80.00 For all formats available, see

www.cambridge.org/9781107100121

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14

Methods for the Physical Sciences Sampling Theory Beyond Bandlimited Systems Yonina C. Eldar Technion – Israel Institute of Technology, Haifa

Covering the fundamental mathematical underpinnings together with engineering principles and applications, this is a comprehensive guide to the theory and practice of sampling. Written from an engineering perspective, it focuses on uniform sampling in shift-invariant spaces and deterministic signals, and includes a wealth of worked examples and end-of-chapter exercises.

‘I must say that this is really a unique book on sampling theory.

The introduction of vector space terminology right from the beginning is a great idea. Starting from classical sampling, the book goes all the way to the most recent breakthroughs including compressive sensing, union-of-subspace setting, and the CoSamp algorithm. Eldar has the right combination of mathematics and practical sense, and she has very good command of the ‘art of writing’.

This, combined with the archival nature of the topic (which has seen seven decades of history), makes the book an invaluable addition to the Cambridge collection of advanced texts in signal processing.’

P. P. Vaidyanathan, California Institute of Technology 2015 247 x 174 mm 836pp 315 b/w illus. 19 tables 198 exercises 978-1-107-00339-2 Hardback £65.00 / US$99.00 For all formats available, see

www.cambridge.org/9781107003392

Statistical Challenges in 21st Century Cosmology (IAU S306) Edited by Alan Heavens Imperial College London

Jean-Luc Starck Commissariat à l’Energie

and Alberto Krone-Martins Universidade de Lisboa

Led by members of the IAU’s Working Group in Astrostatistics and Astroinformatics, this timely volume addresses the intricate mathematical methods needed to extract scientific insights from large and complicated datasets. An essential text for both astronomers and statisticians, it provides a solid foundation to advance new research methods in cosmology.

Proceedings of the International Astronomical Union Symposia and Colloquia

2015 247 x 174 mm 433pp 200 b/w illus. 20 tables 978-1-107-07856-7 Hardback £76.00 / US$125.00 For all formats available, see

www.cambridge.org/9781107078567

Methods for the Social Sciences

Methods for the Social Sciences HIGHLIGHT TEXTBOOK

Statistics Using IBM SPSS An Integrative Approach Third edition Sharon Lawner Weinberg New York University

and Sarah Knapp Abramowitz Drew University, New Jersey

Written in a clear and lively tone, Statistics Using IBM SPSS provides a data-centric approach to statistics with integrated SPSS (version 22) commands, ensuring that students gain both a strong conceptual understanding of statistics and practical facility with statistical software. The third edition features a new chapter on research design.

‘This is the third edition of a very popular and useful text. The focus is on using SPSS in the research process. The chapters have illustrative exercises and meaningful real data problem sets that not only make it convenient for teaching but also provide realistic experiences for students that will stay with them for many years. The book does a very good job presenting the challenge of data analysis and the experience of being a serious researcher looking at important problems; it illustrates how a variety of quantitative methods can be applied to real data to tease out and evaluate the inferences suggested by that data. I strongly recommend

15

this book to instructors of a one- or two-semester introductory statistics course.’

Robert W. Lissitz, University of Maryland

Contents: 1. Introduction; 2. Examining univariate distributions; 3. Measures of location, spread, and skewness; 4. Re-expressing variables; 5. Exploring relationships between two variables; 6. Simple linear regression; 7. Probability fundamentals; 8. Theoretical probability models; 9. The role of sampling in inferential statistics; 10. Inferences involving the mean of a single population when σ is known; 11. Inferences involving the mean when σ is not known: one- and two-sample designs; 12. Research design: introduction and overview; 13. One-way analysis of variance; 14. Two-way analysis of variance; 15. Correlation and simple regression as inferential techniques; 16. An introduction to multiple regression; 17. Nonparametric methods.

2016 253 x 203 mm 592pp 196 b/w illus. 100 tables 417 exercises 978-1-107-46122-2 Paperback £49.99 / US$89.99 Publication February 2016 For all formats available, see

www.cambridge.org/9781107461222

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16

Methods for the Social Sciences Highlight

Highlight

Time Series Analysis for the Social Sciences

Sociology as a Population Science

Janet M. Box-Steffensmeier

John H. Goldthorpe

Ohio State University

University of Oxford

John R. Freeman

John Goldthorpe provides a new rationale for recent developments in sociology, proposing that sociology should be understood as a ‘population science’ and develop as a science in a way which allows for a degree of continuity with the natural sciences, while preserving the field’s distinctiveness.

University of Minnesota

Matthew P. Hitt Louisiana State University

and Jon C. W. Pevehouse University of Wisconsin, Madison

Time Series Analysis for the Social Sciences provides accessible, up-to-date instruction and examples of the core methods in time series econometrics. The book covers ARIMA models, time series regression, unit-root diagnosis, vector autoregressive models, error-correction models, intervention models, fractional integration, ARCH models, structural breaks, and forecasting.

Analytical Methods for Social Research

2015 228 x 152 mm 292pp 93 b/w illus. 30 tables 978-0-521-87116-7 Hardback £55.00 / US$95.00 978-0-521-69155-0 Paperback £19.99 / US$34.99 For all formats available, see

www.cambridge.org/9780521871167

‘In this book, Goldthorpe provides an elegant discussion on the fundamental tenets of sociology as a population science. Based on nine propositions, he explains what sociology is and is not, and defines its logic as a population science, where traditional disciplinary boundaries between sociology and demography, epidemiology and applied economics blur. Sociology as a Population Science should be read by all sociologists engaged in theoretically driven empirical research.

Many will find a solid rationale for the type of sociology that they, in fact, already do and stronger and clearer conceptual bases to pursue their research further on. A precious book.’

Fabrizio Bernardi, European University Institute, and Chair, Board of the European Consortium for Sociological Research 2015 228 x 152 mm 175pp 978-1-107-12783-8 Hardback £44.99 / US$74.99 978-1-107-56731-3 Paperback £14.99 / US$24.99 For all formats available, see

www.cambridge.org/9781107127838

Methods for the Social Sciences Highlight

Mixed Methods Social Networks Research Design and Applications Edited by Silvia Domínguez Northeastern University, Boston

and Betina Hollstein

Applied Choice Analysis Second edition David A. Hensher University of Sydney

John M. Rose University of South Australia

Universität Bremen

and William H. Greene

This volume demonstrates the potential of mixed-methods designs for researching social networks and the utilization of social networks for other research. Mixing methods applies to the combination and integration of qualitative and quantitative methods.

In social network research, mixing methods also applies to the combination of structural and actor-oriented approaches.

A fully updated second edition of this popular book on choice analysis, showing the latest developments in behavioural theory, econometric estimation, and how to apply all methods using the software NLOGIT. Complete with new topics and case studies, this is a valuable resource for students, researchers, professionals and consultants.

‘This transformative book is certain to stimulate a new generation of social network researchers who are comfortable with both ethnographic methods and mathematical models.

A very welcome addition to the bookshelf of social network methods texts!’

New York University

‘This is an enormous book, covering in extraordinary detail all the topics selected by these respected authors.

It represents a substantial update and renewal of the material covered in the first edition. In my opinion it should be on the shelves of anyone dealing with discrete choice models.’

Katherine Faust, University of California, Irvine

Juan de Dios Ortúzar Salas, Pontificia Universidad Católica de Chile

Contents: Part I. General Issues; Part II. Mixed Methods Applications; Part III. New Methodological Approaches Used in Mixed Methods Designs.

Contents: Part I. Getting Started; Part II. Software and Data; Part III. The Suite of Choice Models; Part IV. Advanced Topics.

Structural Analysis in the Social Sciences, 36

2014 228 x 152 mm 404pp 54 b/w illus. 29 tables 978-1-107-02792-3 Hardback £69.99 / US$120.00 978-1-107-63105-2 Paperback £26.99 / US$44.99

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2015 247 x 174 mm 1216pp 150 b/w illus. 182 tables 978-1-107-46592-3 Paperback £55.00 / US$90.00 For all formats available, see

www.cambridge.org/9781107465923

For all formats available, see

www.cambridge.org/9781107027923

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18

Methods for the Social Sciences / Methods for Economics, Finance and Insurance Best-Worst Scaling Theory, Methods and Applications Jordan J. Louviere University of South Australia

Terry N. Flynn TF Choices Ltd

and A. A. J. Marley University of South Australia and University of Victoria, British Columbia

This is the first systematic treatment of the theory and application of bestworst scaling (BWS), an emerging methodology in choice experiments.

The three types of best-worst scaling are introduced and explored, and example applications and case studies illustrate how to implement, apply, and analyze the theory across many different disciplines.

‘Best-Worst Scaling (BWS) has emerged as a novel and innovative method for eliciting preferences and understanding choice behavior.

This book provides researchers and practitioners with a clear understanding of the origins, theory, and use of BWS and contains interesting case studies from a range of disciplines. This excellent collection of papers also provides a fascinating story of how a new research method moves from initial ideas to adoption by researchers in multiple fields worldwide. It is a must-have reference for current users or those interested in learning about BWS.’ W. L. (Vic) Adamowicz, Research Director, Alberta Land Institute, University of Alberta 2015 247 x 174 mm 360pp 49 b/w illus. 130 tables 978-1-107-04315-2 Hardback £39.99 / US$59.99 For all formats available, see

www.cambridge.org/9781107043152

Methods for Economics, Finance and Insurance Predictive Modeling Applications in Actuarial Science Volume 1: Predictive Modeling Techniques Edited by Edward W. Frees University of Wisconsin, Madison

Richard A. Derrig Temple University, Philadelphia

and Glenn Meyers ISO Innovative Analytics, New Jersey

This book is for actuaries and other financial analysts who are developing their expertise in statistics and wish to become familiar with concrete examples of predictive modeling. It emphasizes lifelong learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used by analysts to gain a competitive advantage in situations with complex data.

‘With contributions coming from a wide variety of researchers, professors, and actuaries – including several CAS Fellows – it’s clear that this book will be valuable for any P and C actuary whose main concern is using predictive modeling in his or her own work.’

David Zornek, Actuarial Review

Methods for Economics, Finance and Insurance Contents: Part I. Predictive Modeling Foundations; Part II. Predictive Modeling Methods; Part III. Bayesian and Mixed Modeling; Part IV. Longitudinal Modeling.

International Series on Actuarial Science

2014 247 x 174 mm 563pp 120 b/w illus. 94 tables 26 exercises 978-1-107-02987-3 Hardback £50.00 / US$85.00 For all formats available, see

www.cambridge.org/9781107029873

Contest Theory Incentive Mechanisms and Ranking Methods Milan Vojnović

Contents: 1. Introduction and preview; 2. Standard all-pay contests; 3. Rank order allocation of prizes; 4. Smooth allocation of prizes; 5. Simultaneous contests; 6. Utility sharing and social welfare; 7. Sequential contests; 8. Tournaments; 9. Rating systems; 10. Ranking methods; 11. Appendices.

2016 253 x 177 mm 730pp 187 b/w illus.. 6 tables 978-1-107-03313-9 Hardback £49.99 / US$79.99 For all formats available, see www.cambridge.org/9781107033139

Daragh McInerney AGH University of Science and Technology, Krakow

‘This text provides a comprehensive and engaging treatment of both traditional areas, including innovation prizes, tournaments, and ranking methods, and of recent developments motivated by crowdsourcing and other online services.’

Frank Kelly, University of Cambridge

‘...a careful and unified discussion of the theory of contest design that will be valuable to students and practitioners alike.’

Incentive Mechanisms and Ranking Methods

Stochastic Interest Rates

Using a game-theoretic framework, this unified, comprehensive treatment of contest design in economics and computer science focuses on online applications.

Preston McAfee, Microsoft

CONTEST THEORY Milan Vojnović

Microsoft Research, Cambridge, UK

‘...a delightful and thorough examination of the state of the art in contest modeling, for economists and computer scientists alike.’

19

and Tomasz Zastawniak University of York

Designed for Master’s students and final-year undergraduates, this book strikes the right balance between mathematical rigour and practical application. Carefully chosen examples and exercises help students acquire the necessary skills to deal with interest rate modelling in a real-world setting.

Mastering Mathematical Finance

2015 228 x 152 mm 170pp 25 b/w illus. 10 tables 60 exercises 978-1-107-00257-9 Hardback £49.99 / US$79.99 978-0-521-17569-2 Paperback £23.99 / US$39.99 For all formats available, see

www.cambridge.org/9781107002579

David C. Parkes, Harvard University

eBooks available at www.cambridge.org/ebookstore

20

Methods for Economics, Finance and Insurance Algorithmic and High-Frequency Trading Álvaro Cartea University College London

Sebastian Jaimungal University of Toronto

and José Penalva Universidad Carlos III de Madrid

This cutting-edge textbook shows how to build the advanced mathematical models that underpin modern trading algorithms. ‘[This book] is an important and timely textbook on algorithmic trading.

Human traders in financial markets are an endangered species, gradually replaced by computers and algorithms.

In this new world, designing and coding trading strategies requires knowledge of market microstructure, basic economic principles governing price formation in financial markets, and stylized facts about price dynamics and trading activity. It also requires specific mathematical tools, such as stochastic control, and understanding of how these tools are used to solve trading problems.

Algorithmic and High-Frequency Trading is unique in that it provides a unified treatment of these topics.

I enjoyed reading it and recommend it highly to students or practitioners interested in mathematical models used in algorithmic trading.’

Thierry Foucault, HEC Paris 2015 247 x 174 mm 356pp 5 b/w illus. 75 colour illus. 35 tables 978-1-107-09114-6 Hardback £44.99 / US$64.99 For all formats available, see

www.cambridge.org/9781107091146

Textbook

Granularity Theory with Applications to Finance and Insurance Patrick Gagliardini Universita della Svizzera Italiana, Switzerland

and Christian Gouriéroux University of Toronto

The first comprehensive overview of the granularity theory and its usefulness for risk analysis, statistical estimation, and derivative pricing.

‘Credible portfolio risk assessment requires financial-econometric methods that respect the limitations of finite samples (finite numbers of assets) in real portfolios. Gagliardini and Gouriéroux propose and explore asymptotic expansions (granularity adjustments) that do just that. As expected, their book displays a wonderful clarity of thought and will be highly valued in academic, policy and practitioner circles.’

Francis X. Diebold, Paul F. and Warren S. Miller Professor of Economics, University of Pennsylvania

Contents: 1. The standard asymptotic theorems and their limitations; 2. Gaussian static factor; 3. Static qualitative factor model; 4. Nonlinear dynamic panel-data model; 5. Prediction and basket derivative pricing; 6. Granularity for risk measures.

Themes in Modern Econometrics

2014 228 x 152 mm 202pp 36 b/w illus. 12 tables 978-1-107-07083-7 Hardback £55.00 / US$90.00 978-1-107-66288-9 Paperback £21.99 / US$34.99 For all formats available, see

www.cambridge.org/9781107070837

Methods for Economics, Finance and Insurance Analysis of Panel Data

Highlight

Third edition Cheng Hsiao

Applied Nonparametric Econometrics

University of Southern California

This book provides a comprehensive, coherent, and intuitive review of panel data methodologies that are useful for empirical analysis. Substantially revised from the second edition, it includes two new chapters on modeling crosssectionally dependent data and dynamic systems of equations. Some of the more complicated concepts have been further streamlined.

Review of the previous edition: Researchers will find that the insights that they gain from working through the book’s tougher sections are well worth the effort. The book remains an indispensable and comprehensive reference for panel estimation methods.

David C. Ribar, International Journal of Forecasting Econometric Society Monographs, 54

2015 228 x 152 mm 562pp 7 b/w illus. 9 tables 978-1-107-03869-1 Hardback £75.00 / US$125.00 978-1-107-65763-2 Paperback £32.99 / US$49.99 For all formats available, see

www.cambridge.org/9781107038691

21

Daniel J. Henderson University of Alabama

and Christopher F. Parmeter University of Miami

The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignore the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians, discussing basic to advanced nonparametric methods with applications.

‘A clear and thorough treatment of nonparametric and semiparametric econometrics. The text will be valuable to empirical researchers, who can expand their methodological toolkits without resorting to difficult journal articles. Even advanced topics, such as nonparametric instrumental variables and nonparametric models with panel data, are treated at an accessible level.’

Jeffrey M. Wooldridge, Michigan State University 2015 253 x 177 mm 380pp 81 b/w illus. 34 tables 978-1-107-01025-3 Hardback £75.00 / US$125.00 978-0-521-27968-0 Paperback £30.00 / US$49.99 For all formats available, see

www.cambridge.org/9781107010253

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22

Methods for Economics, Finance and Insurance Expert Adjustments of Model Forecasts Theory, Practice and Strategies for Improvement Philip Hans Franses Erasmus Universiteit Rotterdam

Written for academics and practitioners with an interest in forecasting methodology, this book tests the notion that many forecasters feel they can improve the accuracy of forecasts based on their intuition. Current research is collated to examine ‘expert adjustment’ from an econometric perspective and guidelines for improvement are suggested.

‘All economic and business forecasting involves judgment to some extent, but most books focus on the application of statistical methods. At last we have a book that directly addresses the role of judgment in forecasting and applies a set of rigorous methods to assess its potential strengths and limitations.’

Paul Goodwin, Emeritus Professor, University of Bath 2014 228 x 152 mm 144pp 9 b/w illus. 18 tables 978-1-107-08159-8 Hardback £49.99 / US$74.99

Behind the Model A Constructive Critique of Economic Modeling Peter Spiegler University of Massachusetts, Amherst

This book looks ‘behind the model’ to show how formal models of the economy work - and why they sometimes fail.

‘The 2007–9 global financial crisis and Great Recession demonstrated that the most prestigious economic models were incapable of recognizing, much less preventing, the debacle.

How could this be possible? Peter Spiegler’s critique of mainstream mathematical modeling methodology delivers a compelling explanation.

More important still, Spiegler advances alternative theoretical and empirical methodologies that are capable of reframing economics as a viable scientific endeavor. Behind the Model is a deeply insightful work that should force all open-minded economists to rethink their most basic presuppositions and practices.’

Robert Pollin, Distinguished Professor of Economics and Co-Director, Political Economy Research Institute (PERI), University of Massachusetts, Amherst

For all formats available, see

Contents: Part I. A Constructive Critique of Common Modeling Practice; Part II. The Critique Applied; Part III. A Reform Proposal.

www.cambridge.org/9781107081598

Strategies for Social Inquiry

978-1-107-44161-3 Paperback £19.99 / US$29.99

2015 247 x 174 mm 230pp 5 b/w illus. 5 tables 978-1-107-06966-4 Hardback £64.99 / US$99.99 978-1-107-67780-7 Paperback £22.99 / US$34.99 For all formats available, see

www.cambridge.org/9781107069664

Methods for Economics, Finance and Insurance Key Reference

Highlight

Textbook

Revealed Preference Theory

Almost All about Unit Roots Foundations, Developments, and Applications In Choi

23

Christopher P. Chambers Department of Economics, University of California, San Diego

and Federico Echenique

Sogang University, Seoul

Division of the Humanities and Social Science, California Institute of Technology

Many economic theories depend on the presence or absence of a unit root for their validity, making familiarity with unit roots extremely important to econometric and statistical theory. This book introduces the literature on unit roots in a comprehensive manner to empirical and theoretical researchers in economics and other areas.

The theory of revealed preference has a long, distinguished tradition in economics but lacked a systematic presentation of the theory until now.

This book deals with basic questions in economic theory and studies situations in which empirical observations are consistent or inconsistent with some of the best known economic theories.

‘Choi’s monograph provides an authoritative introduction to and survey of the literature on unit roots, which spans economics, statistics and other fields. Its handbook style and clear treatment make it an extremely convenient graduate-level reference for those working with highly persistent time series data.’

Econometric Society Monographs, 56

2016 228 x 152 mm 224pp 33 b/w illus.

978-1-107-08780-4 Hardback £54.99 / US$89.99 978-1-107-45811-6 Paperback £21.99 / US$34.99 For all formats available, see

www.cambridge.org/9781107087804

J. Isaac Miller, University of Missouri

Contents: 1. Introduction; 2. Inference on unit roots: basic methods; 3. Unit root tests under various model specifications; 4. Alternative approaches to inference on unit roots; 5. Other issues related to unit roots; 6. Seasonal unit roots; 7. Panel unit roots.

Themes in Modern Econometrics

2015 228 x 152 mm 296pp 27 tables 978-1-107-09733-9 Hardback £60.00 / US$95.00 978-1-107-48250-0 Paperback £24.99 / US$39.99 For all formats available, see

www.cambridge.org/9781107097339

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24

Methods for Economics, Finance and Insurance / Probability Textbook

Dynamic Economic Analysis Deterministic Models in Discrete Time Gerhard Sorger Universität Wien, Austria

For advanced students in economics, this textbook provides a clear and concise introduction to dynamic economic theory and analysis.

‘This book offers a comprehensive vision of economic dynamics suitable for graduate students and professionals alike. Gerhard Sorger is a leading researcher with a flair for presenting mathematically challenging theories carefully and rigorously. His text emphasizes the interplay between formal theory and applications with detailed developments of a catalogue of economic models and examples drawn from macroeconomics, growth theory and game theory. There is no other single book readily accessible in the economics literature covering the same wide range of deterministic dynamics and optimization theories with detailed illustrations of those theories in action. It is accessible to students engaged in a self-study program for students engaging with dynamical systems for the first time.

Better yet, it offers the topics and treatments for a course in dynamics.’

Robert A. Becker, Indiana University, Bloomington

Contents: Part I. Difference Equations; Part II. Dynamic Optimization.

2015 246 x 189 mm 301pp 30 b/w illus.

978-1-107-08329-5 Hardback £65.00 / US$99.00 978-1-107-44379-2 Paperback £24.99 / US$39.99 For all formats available, see

www.cambridge.org/9781107083295

Probability Probability The Classical Limit Theorems Henry McKean New York University

McKean constructs a clear path through the subject and sheds light on a variety of interesting topics in which probability theory plays a key role. Anyone who wants to learn or use probability will benefit from reading this book. ‘...packs a great deal of material into a moderate-sized book, starting with a synopsis of measure theory and ending with a taste of current research into random matrices and number theory. The book ranges more widely than the title might suggest...There are numerous exercises sprinkled throughout the book. Most of these are exhortations to fill in details left out of the main discussion or illustrative examples. The exercises are a natural part of the book, unlike the exercises in so many books that were apparently grafted on after-the-fact at a publisher’s insistence.

John D. Cook, MAA Reviews

Contents: Preface; 1. Preliminaries; 2. Bernoulli trials; 3. The standard random walk; 4. The standard random walk in higher dimensions; 5. LLN, CLT, iterated log, and arcsine in general; 6. Brownian motion; 7. Markov chains; 8. The ergodic theorem; 9. Communication over a noisy channel; 10. Equilibrium statistical mechanics; 11. Statistical mechanics out of equilibrium; 12. Random matrices; Bibliography; Index.

2014 228 x 152 mm 488pp 122 b/w illus. 260 exercises 978-1-107-05321-2 Hardback £60.00 / US$99.00 978-1-107-62827-4 Paperback £27.99 / US$45.99 For all formats available, see

www.cambridge.org/9781107053212

Probability The Surprising Mathematics of Longest Increasing Subsequences Dan Romik

Noise Sensitivity of Boolean Functions and Percolation Christophe Garban Université Lyon I

University of California, Davis

and Jeffrey E. Steif

This book presents for the first time to a graduate-level readership recent groundbreaking developments in probability and combinatorics related to the longest increasing subsequence problem. Its detailed, playful presentation provides a motivating entry to elegant mathematical ideas that are of interest to every mathematician and to many computer scientists, physicists and statisticians.

This account of the new and exciting area of noise sensitivity of Boolean functions – in particular applied to critical percolation – is designed for graduate students and researchers in probability theory, discrete mathematics, and theoretical computer science.

It assumes a basic background in probability theory and integration theory.

Each chapter ends with exercises.

‘The story of longest monotone subsequences in permutations has been, for six decades, one of the most beautiful in mathematics,.

With its connections to many areas of mathematics, to the Riemann hypothesis, and to high-energy physics we cannot foresee where the story is heading. Dan Romik tells the tale thus far – and teaches its rich multifaceted mathematics, a blend of probability, combinatorics, analysis, and algebra – in a wonderful way.’

Gil Kalai, Hebrew University of Jerusalem Institute of Mathematical Statistics Textbooks, 4

2015 228 x 152 mm 366pp 3 b/w illus. 94 exercises 978-1-107-07583-2 Hardback £65.00 / US$99.00 978-1-107-42882-9 Paperback £27.99 / US$39.99 For all formats available, see

www.cambridge.org/9781107075832

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Chalmers University of Technology, Gothenberg

‘Presented in an orderly, accessible manner, this book provides an excellent exposition of the general theory of noise sensitivity and its beautiful and deep manifestation in two dimensional critical percolation.

The authors, both of whom are major contributors to the theory, have produced a very thoughtful work, bringing the intuition and motivations first. The book elegantly unfolds the story of integrating the general theory of noise sensitivity into a concrete study, allowing for a new understanding of the percolation process.’

Itai Benjamini, Weizmann Institute of Science, Israel Institute of Mathematical Statistics Textbooks, 5

2015 228 x 152 mm 222pp 29 b/w illus. 75 exercises 978-1-107-07643-3 Hardback £60.00 / US$99.00 978-1-107-43255-0 Paperback £21.99 / US$34.99 For all formats available, see

www.cambridge.org/9781107076433

eBooks available at www.cambridge.org/ebookstore

26

Probability Probability and Statistics by Example Volume 1: Basic Probability and Statistics Second edition Yuri Suhov University of Cambridge

and Mark Kelbert Swansea University

A valuable resource for students and teachers alike, this second edition is packed with over 200 worked examples and exam questions with solutions.

It promotes a deep understanding of the subject rather than a superficial knowledge of the theory, equipping students to solve problems in practice and under exam conditions.

Review of previous edition: ‘… all the ingredients that contribute to making a good lecture are in the book: well-explained theory, interesting examples, and funny jokes and amusing stories about famous probabilists and statisticians. The authors also give rhythm to the flow of the subjects, making the volume a very pleasant book to read.’

Emanuele Taufer, Mathematical Reviews

Contents: Part I. Basic Probability; Part II. Basic Statistics.

2014 247 x 174 mm 470pp 40 b/w illus. 210 exercises 978-1-107-60358-5 Paperback £49.99 / US$84.99 For all formats available, see

www.cambridge.org/9781107603585

Quantum Stochastics Mou-Hsiung Chang Mathematical Sciences Division, US Army Research Office

This book provides a systematic, self-contained treatment of the theory of quantum probability and quantum Markov processes for graduate students and researchers. Building a framework that parallels the development of classical probability, it aims to help readers up the steep learning curve of the quantum theory.

‘This excellent introductory book on quantum stochastics is most timely – the highly interdisciplinary book needed in this century. Dr Chang has organized and presented the material in a systematic and coherent manner … [The] material is self-contained and self-readable; ideas, concepts and topics are up-to-date, well-motivated, well presented and well associated with the classical approach: starting from the idea of a quantum system, to quantum probability space, measure, processes with quantum stochastic calculus, quantum stochastic differential equations, quantum stability, quantum semigroups, and so on. I am looking forward to using this book for my graduate course on quantum stochastic dynamic systems.

I will also highly recommend it to my students and colleagues.’

G. S. Ladde, University of South Florida, and founder and editor of the Journal of Stochastic Analysis and Applications Cambridge Series in Statistical and Probabilistic Mathematics, 37

2015 253 x 177 mm 424pp 978-1-107-06919-0 Hardback £55.00 / US$85.00 For all formats available, see

www.cambridge.org/9781107069190

Probability Probability on Real Lie Algebras

Introduction to Random Graphs

Uwe Franz

Alan Frieze

Université de Franche-Comté

Carnegie Mellon University, Pennsylvania

and Nicolas Privault

and Michal Karoński

Nanyang Technological University, Singapore

Uniwersytet im. Adama Mickiewicza w Poznaniu, Poland

This monograph is a progressive introduction to non-commutativity in probability theory, summarizing and synthesizing recent results about classical and quantum stochastic processes on Lie algebras. This book will appeal to advanced undergraduate and graduate students interested in the relations between algebra, probability, and quantum theory.

Cambridge Tracts in Mathematics, 206

2016 228 x 152 mm 302pp 2 b/w illus. 27 exercises 978-1-107-12865-1 Hardback £79.99 / US$125.00

27

This book covers random graphs from the basic to the advanced and will appeal to anyone interested in combinatorics, applied probability or theoretical computer science. Having read this book, the reader should be in a good position to pursue research in the area.

Contents: Part I. Basic Models; Part II. Basic Model Extensions; Part III. Other Models; Part IV. Tools and Methods.

For all formats available, see

2015 228 x 152 mm 478pp 25 b/w illus. 190 exercises 978-1-107-11850-8 Hardback £49.99 / US$79.99

www.cambridge.org/9781107128651

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www.cambridge.org/9781107118508

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28

Probability Random Graphs, Geometry and Asymptotic Structure

Convergence of One-Parameter Operator Semigroups

Michael Krivelevich

In Models of Mathematical Biology and Elsewhere Adam Bobrowski

Tel-Aviv University

Konstantinos Panagiotou Universität Munchen

Mathew Penrose University of Bath

and Colin McDiarmid University of Oxford

Edited by Nikolaos Fountoulakis University of Birmingham

and Dan Hefetz University of Birmingham

A self-contained and concise introduction to recent developments, particularly those of a geometric and topological nature, in the theory of random graphs. Such material is seldom covered in the formative study of young combinatorialists and probabilists, making this essential reading for beginning researchers in these fields.

Contents: Part I. Long Paths and Hamiltonicity in Random Graphs; Part II. Random Graphs from Restricted Classes; Part III. Lectures on Random Geometric Graphs; Part IV. On Random Graphs from a Minor-closed Class.

London Mathematical Society Student Texts, 84

2016 228 x 152 mm 160pp 2 b/w illus. 1 table 978-1-107-13657-1 Hardback £49.99 / US$84.99 978-1-316-50191-7 Paperback £24.99 / US$39.99 Publication April 2016 For all formats available, see

www.cambridge.org/9781107136571

Politechnika Lubelska, Poland

Written by a leading expert in the field, this book presents the classical theory of convergence of semigroups and then uses real examples to show how it can be applied to models of mathematical biology as well as other branches of mathematics.

Contents: Part I. Regular Convergence; Part II. Irregular Convergence; Part III. Convergence of Cosine Families; Part IV. Appendices.

New Mathematical Monographs, 30

2016 228 x 152 mm 439pp 60 b/w illus. 9 colour illus. 160 exercises 978-1-107-13743-1 Hardback £89.99 / US$140.00 Publication March 2016 For all formats available, see

www.cambridge.org/9781107137431

Probability / Applied Probability and Stochastic Networks Random Matrix Theory, Interacting Particle Systems, and Integrable Systems Edited by Percy Deift New York University, Courant Institute of Mathematical Sciences

29

Applied Probability and Stochastic Networks

and Peter Forrester University of Melbourne

This volume, based on the Fall 2010 MSRI program, includes review articles, research contributions on long-standing questions on universalities of Wigner matrices and beta-ensembles, and other core aspects of random matrix theory such as integrability and free probability theory.

Contributors: Ge rnot Akemann, Michael Phillips, Jinho Baik, Dong Wang, Pavel Bleher, Karl Liechty, Alexei Borodin, Tom Claeys, Tamara Grava, Percy Deift, Alexander Its, Igor Krasovsky, Maurice Duits, Arno B. J. Kuijlaars, Man Yue Mo, Nicholas M. Ercolani, Peter Forrester, Josselin Garnier, Knut Solna, John Harnad, Alexander Orlov, Doron Lubinsky, Mylene Maida, Jonathan Novak, Neil O’Connell, Christian Pfrang, Govind Menon, Jeremy Quastel, Tomohiro Sasamoto, Mariya Shcherbina, Herbert Spohn, Kazamasa Takeuchi, Terence Tao, Van Vu Mathematical Sciences Research Institute Publications, 65

2015 234 x 156 mm 540pp 978-1-107-07992-2 Hardback £75.00 / US$120.00 For all formats available, see

Textbook

Network Science Albert-László Barabási Northeastern University, Boston

Illustrated throughout in full colour, this pioneering textbook, spanning a wide range of disciplines from physics to the social sciences, is the only book needed for an introduction to network science.

In modular format, with clear delineation between undergraduate and graduate material, its unique design is supported by extensive online resources.

Contents: Preface; Personal introduction; 1. Introduction; 2. Graph theory; 3. Random networks; 4. The scale-free property; 5. The Barabási–Albert model; 6. Evolving networks; 7. Degree correlation; 8. Network robustness; 9. Communities; 10. Spreading phenomena; Index.

2016 246 x 189 mm 498pp 371 colour illus. 12 tables 30 exercises 978-1-107-07626-6 Hardback £34.99 / US$59.99 Publication March 2016 For all formats available, see

www.cambridge.org/9781107076266

www.cambridge.org/9781107079922

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30

Applied Probability and Stochastic Networks An Introduction to Computational Stochastic PDEs Gabriel J. Lord Heriot-Watt University, Edinburgh

Catherine E. Powell University of Manchester

and Tony Shardlow University of Bath

This comprehensive introduction to stochastic partial differential equations incorporates the effects of randomness into real-world models, offering graduate students and researchers powerful tools for understanding uncertainty quantification for risk analysis. MATLAB codes are included, so that readers can perform computations themselves and solve the test problems discussed.

Contents: Part I. Deterministic Differential Equations; Part II. Stochastic Processes and Random Fields; Part III. Stochastic Differential Equations.

Cambridge Texts in Applied Mathematics, 50

2014 247 x 174 mm 516pp 107 b/w illus. 16 colour illus. 222 exercises 978-0-521-89990-1 Hardback £84.99 / US$140.00 978-0-521-72852-2 Paperback £39.99 / US$64.99 For all formats available, see

www.cambridge.org/9780521899901

An Introduction to Stochastic Dynamics Jinqiao Duan Illinois Institute of Technology

This book serves as a concise introductory text on stochastic dynamics for applied mathematicians. Rich with examples, illustrations, and exercises with their solutions, it provides an accessible introduction to concepts and techniques for describing, quantifying, and understanding dynamics under uncertainty, from analytical, deterministic, structural and numerical perspectives.

‘Jinqiao Duan’s book introduces the reader to the actively developing theory of stochastic dynamics through well-chosen examples that provide an overview, useful insights, and intuitive understanding of an often technically complicated topic.’

P. E. Kloeden, Goethe University, Frankfurt am Main Cambridge Texts in Applied Mathematics, 51

2015 247 x 174 mm 312pp 60 b/w illus. 100 exercises 978-1-107-07539-9 Hardback £94.99 / US$150.00 978-1-107-42820-1 Paperback £41.99 / US$64.99 For all formats available, see

www.cambridge.org/9781107075399

Applied Probability and Stochastic Networks Workload Modeling for Computer Systems Performance Evaluation Dror G. Feitelson Hebrew University of Jerusalem

This book closes the gap between experts and practitioners, emphasizing the intuition and reasoning behind definitions and derivations related to evaluations of computer systems performance. Readers will learn to analyze collected workload data, derive statistical models, including skewed marginal distributions and correlations, and consider generative models and feedback from systems.

2015 253 x 177 mm 564pp 182 b/w illus. 90 colour illus. 18 tables 978-1-107-07823-9 Hardback £50.00 / US$75.00 For all formats available, see

www.cambridge.org/9781107078239

31

Partially Observed Markov Decision Processes From Filtering to Controlled Sensing Vikram Krishnamurthy University of British Columbia, Vancouver

This book covers formulation, algorithms, and structural results of partially observed Markov decision processes, whilst linking theory to real-world applications in controlled sensing.

Computations are kept to a minimum, enabling students and researchers in engineering, operations research, and economics to understand the methods and determine the structure of their optimal solution.

Contents: Part I. Stochastic Models and Bayesian Filtering; Part II. Partially Observed Markov Decision Processes.

Models and Algorithms; Part III. Partially Observed Markov Decision Processes; Part IV. Stochastic Approximation and Reinforcement Learning.

2016 247 x 174 mm 432pp 47 b/w illus. 5 tables 978-1-107-13460-7 Hardback £59.99 / US$99.99 Publication March 2016 For all formats available, see

www.cambridge.org/9781107134607

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32

Applied Probability and Stochastic Networks / Optimization, OR and Risk Big Data over Networks Edited by Shuguang Cui Texas A & M University

Alfred O. Hero, III

Optimization, OR and Risk

University of Michigan, Ann Arbor

Textbook

Zhi-Quan Luo

Optimization Models

University of Minnesota

and José M. F. Moura Carnegie Mellon University, Pennsylvania

Written by experts in the field, this pioneering text is the first to examine the crucial interaction between big data and three diverse networks: communication, social and biological.

Using critical mathematical tools and state-of-the-art research results, it is an essential reference for graduate students, scientific researchers and industry practitioners.

Contents: Part I. Mathematical Foundations; Part II. Big Data over Cyber Networks; Part III. Big Data over Social Networks; Part IV. Big Data over Biological Networks.

2016 247 x 174 mm 457pp 115 b/w illus. 30 tables 978-1-107-09900-5 Hardback £64.99 / US$89.99 For all formats available, see

www.cambridge.org/9781107099005

Giuseppe C. Calafiore Politecnico di Torino

and Laurent El Ghaoui University of California, Berkeley

Emphasizing practical understanding over the technicalities of specific algorithms, this elegant textbook teaches students how to recognize, simplify, model and solve optimization problems – and apply these basic principles to their own projects. Accompanied by an online solution manual, accessible only to instructors.

‘In Optimization Models, Calafiore and El Ghaoui have created a beautiful and very much needed on-ramp to the world of modern mathematical optimization and its wide range of applications. They lead an undergraduate, with not much more than basic calculus behind her, from the basics of linear algebra all the way to modern optimization-based machine learning, image processing, control, and finance, to name just a few applications.’

Stephen Boyd, Stanford University

Contents: Part I. Linear Algebra; Part II. Convex Optimization; Part III. Applications.

2014 246 x 189 mm 650pp 352 b/w illus. 126 exercises 978-1-107-05087-7 Hardback £48.00 / US$69.99 For all formats available, see

www.cambridge.org/9781107050877

Optimization, OR and Risk Textbook

Optimization in Practice with MATLAB® For Engineering Students and Professionals Achille Messac Mississippi State University

This textbook is designed for undergraduate and graduate students and practitioners interested in learning optimization. The presentation is enriched with a robust set of real-world examples and practical exercises. Using MATLAB® software is encouraged throughout. The instructor is supported by a complete solutions manual and PowerPoint slides with animations.

Contents: Part I. Helpful Preliminaries; Part II. Using Optimization – the Road Map; Part III. Using Optimization – Practical Essentials; Part IV. Going Deeper; Part V. More Advanced Topics in Optimization.

2015 253 x 177 mm 494pp 159 b/w illus. 61 tables 191 exercises 978-1-107-10918-6 Hardback £45.00 / US$99.00 For all formats available, see

www.cambridge.org/9781107109186

33

An Introduction to Polynomial and Semi-Algebraic Optimization Jean Bernard Lasserre Centre National de la Recherche Scientifique (CNRS), Toulouse

This is the first comprehensive introduction to the powerful moment approach for solving global optimization problems. Graduate students, engineers and researchers entering the field can use this book to understand, experiment with and master this new approach through the simple worked examples provided.

‘This monograph may be considered as a comprehensive introduction to solving global optimization problems described by polynomials and even semi-algebraic functions. The book is accompanied by a MATLAB® freeware software that implements the described methodology … The well written and extensive introduction may help the reader to knowingly use the book.’

Jerzy Ombach, Zentralblatt MATH John von Neumann Theory Prize 2015

Contents: Part I. Positive Polynomials and Moment Problems; Part II. Polynomial and Semi-algebraic Optimization; Part III. Specializations and Extensions.

Cambridge Texts in Applied Mathematics, 52

2015 228 x 152 mm 354pp 15 b/w illus. 2 colour illus.

978-1-107-06057-9 Hardback £70.00 / US$120.00 978-1-107-63069-7 Paperback £35.00 / US$60.00 For all formats available, see

www.cambridge.org/9781107060579

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34

Optimization, OR and Risk Convex Optimization of Power Systems

Models of Decision-Making

Joshua Adam Taylor

Simplifying Choices Paul Weirich

University of Toronto

This mathematically rigorous guide to convex optimization for power systems engineering includes convex models for a variety of real-world applications, and a selection of problems and practical examples. An invaluable resource for students and researchers from industry and academia in power systems, optimization, and control.

University of Missouri, Columbia

2015 247 x 174 mm 209pp 31 b/w illus. 3 tables 43 exercises 978-1-107-07687-7 Hardback £60.00 / US$95.00

2015 228 x 152 mm 276pp 15 b/w illus. 7 tables 978-1-107-07779-9 Hardback £60.00 / US$95.00

For all formats available, see

For all formats available, see

www.cambridge.org/9781107076877

The Prisoner’s Dilemma Edited by Martin Peterson Texas A & M University

This volume of new essays examines and explores the ramifications of the Prisoner’s Dilemma, one of the most debated thought experiments in philosophy and the social sciences. It is a vital and accessible resource for students and scholars in philosophy, game theory, economics, and the social and political sciences.

Classic Philosophical Arguments

2015 247 x 174 mm 306pp 42 b/w illus. 7 tables 978-1-107-04435-7 Hardback £64.99 / US$110.00 978-1-107-62147-3 Paperback £18.99 / US$29.99 For all formats available, see

www.cambridge.org/9781107044357

Classical decision theory evaluates entire worlds, specified so as to include everything a decision-maker cares about.

Paul Weirich argues that we need only compare small parts of the options we face in order to make a rational decision, and explains how we can simplify and streamline our choices.

www.cambridge.org/9781107077799

Also of Interest

Also of Interest Numerical Analysis Using R Solutions to ODEs and PDEs Graham W. Griffiths City University London

This book presents the latest numerical solutions to initial value problems and boundary value problems described

by ODEs and PDEs. The author offers practical methods for a wide range of problems and illustrates them in the increasingly popular open source language R, allowing integration with more statistical methods.

‘Graham W. Griffiths has produced an outstanding contribution to scientific computation, specifically, the numerical solution of a series of real-world ODE/PDE models. The format of each chapter, i.e. a detailed discussion of the origin of each model, a listing of the commented R routines with background for the numerical algorithms, and an analysis of the computed solutions, permits the reader to immediately understand and execute each model.’

35

Textbook

Learning Scientific Programming with Python Christian Hill University College London

Learn to master basic programming tasks from scratch with real-life scientific examples drawn from many different areas of science and engineering. This complete introduction to using Python teaches Numpy, SciPy and Matplotlib libraries and is supported by extensive online resources to provide a targeted package for students and researchers.

Contents: 1. Introduction; 2. The core Python language I; 3. Interlude: simple plotting with pylab; 4. The core Python language II; 5. IPython and IPython notebook; 6. NumPy; 7. Matplotlib; 8. SciPy; 9. General scientific programming; Appendix A; Solutions; Index.

2016 247 x 174 mm 457pp 93 b/w illus. 52 tables 150 exercises 978-1-107-07541-2 Hardback £69.99 / US$99.99 978-1-107-42822-5 Paperback £27.99 / US$44.99 For all formats available, see

www.cambridge.org/9781107075412

W. E. Schiesser, Lehigh University, Pennsylvania 2016 253 x 177 mm 500pp 182 b/w illus. 15 colour illus.

978-1-107-11561-3 Hardback c. £49.99 / c. US$79.99 Publication April 2016 For all formats available, see

www.cambridge.org/9781107115613

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36

Also of Interest Essentials of Programming in Mathematica® Paul Wellin

This introduction is suitable for someone who has little or no background in Mathematica®, or who has some experience using other languages such as C, Java, or Perl. Starting from first principles, this example-driven text contains material from disciplines as varied as linguistics, bioinformatics, geometry, computer science, and many more.

2015 246 x 189 mm 436pp 45 b/w illus. 190 colour illus. 350 exercises 978-1-107-11666-5 Hardback £39.99 / US$59.99 For all formats available, see

www.cambridge.org/9781107116665

Notes

37

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