Introducing Monte Carlo Methods with R (Use R!) [Paperback]

Christian P. Robert (Author), George Casella (Author)

Book Description

ISBN-10: 1441915753 | ISBN-13: 978-1441915757 | Publication Date: December 10, 2009 | Edition: 1

Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison. While this book constitutes a comprehensive treatment of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella (2004). Similarly, the more exploratory and less stable solutions are not covered here. This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis {Hastings and Gibbs algorithms, and adaptive algorithms. All chapters include exercises and all R programs are available as an R package called mcsm. The book appeals to anyone with a practical interest in simulation methods but no previous exposure. It is meant to be useful for students and practitioners in areas such as statistics, signal processing, communications engineering, control theory, econometrics, finance and more. The programming parts are introduced progressively to be accessible to any reader.

Editorial Reviews

Review

From the reviews: “Robert and Casella’s new book uses the programming language R, a favorite amongst (Bayesian) statisticians to introduce in eight chapters both basic and advanced Monte Carlo techniques … . The book could be used as the basic textbook for a semester long course on computational statistics with emphasis on Monte Carlo tools … . useful for (and should be next to the computer of) a large body of hands on graduate students, researchers, instructors and practitioners … .” (Hedibert Freitas Lopes, Journal of the American Statistical Association, Vol. 106 (493), March, 2011) “Chapters focuses on MCMC methods the Metropolis–Hastings algorithm, Gibbs sampling, and monitoring and adaptation for MCMC algorithms. … There are exercises within and at the end of all chapters … . Overall, the level of the book makes it suitable for graduate students and researchers. Others who wish to implement Monte Carlo methods, particularly MCMC methods for Bayesian analysis will also find it useful.” (David Scott, International Statistical Review, Vol. 78 (3), 2010)

From the Back Cover

Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison. While this book constitutes a comprehensive treatment of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella (2004). Similarly, the more exploratory and less stable solutions are not covered here. This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis {Hastings and Gibbs algorithms, and adaptive algorithms. All chapters include exercises and all R programs are available as an R package called mcsm. The book appeals to anyone with a practical interest in simulation methods but no previous exposure. It is meant to be useful for students and practitioners in areas such as statistics, signal processing, communications engineering, control theory, econometrics, finance and more. The programming parts are introduced progressively to be accessible to any reader. Christian P. Robert is Professor of Statistics at Université Paris Dauphine, and Head of the Statistics Laboratory of CREST, both in Paris, France. He has authored more than 150 papers in applied probability, Bayesian statistics and simulation methods. He is a fellow of the Institute of Mathematical Statistics and the recipient of an IMS Medallion. He has authored eight other books, including The Bayesian Choice which received the ISBA DeGroot Prize in 2004, Monte Carlo Statistical Methods with George Casella, and Bayesian Core with Jean-Michel Marin. He has served as Joint Editor of the Journal of the Royal Statistical Society Series B, as well as an associate editor for most major statistical journals, and was the 2008 ISBA President. George Casella is Distinguished Professor in the Department of Statistics at the University of Florida. He is active in both theoretical and applied statistics, is a fellow of the Institute of Mathematical Statistics and the American Statistical Association, and a Foreign Member of the Spanish Royal Academy of Sciences. He has served as Theory and Methods Editor of the Journal of the American Statistical Association, as Executive Editor of Statistical Science, and as Joint Editor of the Journal of the Royal Statistical Society Series B. In addition to books with Christian Robert, he has written Variance Components, 1992, with S.R. Searle and C.E. McCulloch; Statistical Inference, Second Edition, 2001, with Roger Berger; and Theory of Point Estimation, Second Edition, 1998, with Erich Lehmann. His latest book is Statistical Design 2008.

Product Details

• Paperback: 304 pages
• Publisher: Springer Verlag; 1 edition (December 10, 2009)
• Language: English
• ISBN-10: 1441915753
• ISBN-13: 978-1441915757
• Product Dimensions: 9.2 x 6.1 x 0.9 inches
• Shipping Weight: 15.5 ounces (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #172,809 in Books (See Top 100 in Books)

More About the Author

Christian P. Robert is Professor of Statistics at Université Paris-Dauphine since 2000, as well as the head of the Stat Lab at CREST, INSEE, Paris. He became a senior member of the Institut Universitaire de France in OCtober 2010. He was co-editor of the Journal of the Royal Statistical Society, Series B, from 2006 till 2010. He was the president of the International Society for Bayesian Analysis (ISBA) in 2008. His latest book is Introducing Monte Carlo Methods with R translated into French in 2011 (and soon to be translated into Japanese). He is currently working on the new editions of Bayesian Core and Monte Carlo Statistical Methods.

Customer Reviews

Most Helpful Customer Reviews

3 of 3 people found the following review helpful:

4.0 out of 5 stars Quite Good, December 14, 2011

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Guy - See all my reviews

This review is from: Introducing Monte Carlo Methods with R (Use R!) (Paperback)

I guess this text is supposed to be the applied, less theoretical, little brother of Monte Carlo Statistical Methods which was written by the same authors. The amount of material seems appropriate for a 1 semester crash-course in applications, and in my opinion it does this quite well. Proofs here are replaced either by heuristics or by nothing at all, which is fine for someone who just wants to run their algorithms without looking too far under the hood.

There is a corresponding R package which contains all the code from the examples, so one can step through the examples in the text. Actually playing with these techniques is crucial for getting a feel for how they work and how to use them and this is probably as close as the authors can get to bringing that experience to the reader.

If you know absolutely nothing about Monte Carlo methods, this book will give you a taste of what they are and what they can be used for. It is a pretty short text and I would consider it fairly light reading so it should be worth the time investment. If you think that you probably need an in-depth knowledge of these methods, MCSM is probably a better use of time (it is also pretty easy reading for a math book).