Monte Carlo Statistical Methods (Springer Texts in Statistics) by Christian P. Robert (PDF)

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We have sold 4300 copies worldwide of the first edition (1999).This new edition contains five completely new chapters covering new developments.

User’s Reviews

Editorial Reviews: Review From the reviews:MATHEMATICAL REVIEWS”Although the book is written as a textbook, with many carefully worked out examples and exercises, it will be very useful for the researcher since the authors discuss their favorite research topics (Monte Carlo optimization and convergence diagnostics) going through many relevant references…This book is a comprehensive treatment of the subject and will be an essential reference for statisticians working with McMC.”From the reviews of the second edition:”Only 2 years after its first edition this carefully revised second edition accounts for the rapid development in this field…This book can be highly recommended for students and researchers interested in learning more about MCMC methods and their background.” Biometrics, March 2005″This is a comprehensive book for advanced graduate study by statisticians.” Technometrics, May 2005″This excellent text is highly recommended…” Short Book Reviews of the ISI, April 2005″This book provides a thorough introduction to Monte Carlo methods in statistics with an emphasis on Markov chain Monte Carlo methods. … Each chapter is concluded by problems and notes. … The book is self-contained and does not assume prior knowledge of simulation or Markov chains. …. on the whole it is a readable book with lots of useful information.” (Søren Feodor Nielsen, Journal of Applied Statistics, Vol. 32 (6), August, 2005)”This revision of the influential 1999 text … includes changes to the presentation in the early chapters and much new material related to MCMC and Gibbs sampling. The result is a useful introduction to Monte Carlo methods and a convenient reference for much of current methodology. … The numerous problems include many with analytical components. The result is a very useful resource for anyone wanting to understand Monte Carlo procedures. This excellent text is highly recommended … .” (D.F. Andrews, Short Book Reviews, Vol. 25 (1), 2005)”You have to practice statistics on a desert island not to know that Markov chain Monte Carlo (MCMC) methods are hot. That situation has caused the authors not only to produce a new edition of their landmark book but also to completely revise and considerably expand it. … This is a comprehensive book for advanced graduate study by statisticians.” (Technometrics, Vol. 47 (2), May, 2005)”This remarkable book presents a broad and deep coverage of the subject. … This second edition is a considerably enlarged version of the first. Some subjects that have matured more rapidly in the five years following the first edition, like reversible jump processes, sequential MC, two-stage Gibbs sampling and perfect sampling have now chapters of their own. … the book is also very well suited for self-study and is also a valuable reference for any statistician who wants to study and apply these techniques.” (Ricardo Maronna, Statistical Papers, Vol. 48, 2006)”This second edition of ‘Monte Carlo Statistical Methods’ has appeared only five years after the first … the new edition aims to incorporate recent developments. … Each chapter includes sections with problems and notes. … The style of the presentation and many carefully designed examples make the book very readable and easily accessible. It represents a comprehensive account of the topic containing valuable material for lecture courses as well as for research in this area.” (Evelyn Buckwar, Zentrablatt MATH, Vol. 1096 (22), 2006)”This is a useful and utilitarian book. It provides a catalogue of modern Monte carlo based computational techniques with ultimate emphasis on Markov chain Monte Carlo (MCMC) … . an excellent reference for anyone who is interested in algorithms for various modes of Markov chain (MC) methodology … . a must for any researcher who believes in the importance of understanding what goes on inside of the MCMC ‘black box.’ … I recommend the book to all who wish to learn about statistical simulation.” (Wesley O. Johnson, Journal of the American Statistical Association, Vol. 104 (485), March, 2009) From the Back Cover Monte Carlo statistical methods, particularly those based on Markov chains, are now an essential component of the standard set of techniques used by statisticians. This new edition has been revised towards a coherent and flowing coverage of these simulation techniques, with incorporation of the most recent developments in the field. In particular, the introductory coverage of random variable generation has been totally revised, with many concepts being unified through a fundamental theorem of simulationThere are five completely new chapters that cover Monte Carlo control, reversible jump, slice sampling, sequential Monte Carlo, and perfect sampling. There is a more in-depth coverage of Gibbs sampling, which is now contained in three consecutive chapters. The development of Gibbs sampling starts with slice sampling and its connection with the fundamental theorem of simulation, and builds up to two-stage Gibbs sampling and its theoretical properties. A third chapter covers the multi-stage Gibbs sampler and its variety of applications. Lastly, chapters from the previous edition have been revised towards easier access, with the examples getting more detailed coverage.This textbook is intended for a second year graduate course, but will also be useful to someone who either wants to apply simulation techniques for the resolution of practical problems or wishes to grasp the fundamental principles behind those methods. The authors do not assume familiarity with Monte Carlo techniques (such as random variable generation), with computer programming, or with any Markov chain theory (the necessary concepts are developed in Chapter 6). A solutions manual, which covers approximately 40% of the problems, is available for instructors who require the book for a course.Christian P. Robert is Professor of Statistics in the Applied Mathematics Department at Université Paris Dauphine, France. He is also Head of the Statistics Laboratory at the Center for Research in Economics and Statistics (CREST) of the National Institute for Statistics and Economic Studies (INSEE) in Paris, and Adjunct Professor at Ecole Polytechnique. He has written three other books and won the 2004 DeGroot Prize for The Bayesian Choice, Second Edition, Springer 2001. He also edited Discretization and MCMC Convergence Assessment, Springer 1998. He has served as associate editor for the Annals of Statistics, Statistical Science and the Journal of the American Statistical Association. He is a fellow of the Institute of Mathematical Statistics, and a winner of the Young Statistician Award of the Société de Statistique de Paris in 1995.George Casella is Distinguished Professor and Chair, Department of Statistics, University of Florida. He has served as the Theory and Methods Editor of the Journal of the American Statistical Association and Executive Editor of Statistical Science. He has authored three other textbooks: Statistical Inference, Second Edition, 2001, with Roger L. Berger; Theory of Point Estimation, 1998, with Erich Lehmann; and Variance Components, 1992, with Shayle R. Searle and Charles E. McCulloch. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association, and an elected fellow of the International Statistical Institute.

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐Monte Carlo theory is not an easy topic. This is easily a 2nd year MS/PhD level course. Anyone delving into the topic should be aware that it will require knowledge of MLE, sufficiency, Bayesian point estimation theory, convergence, etc. A good background to have before jumping into this topic would be material covered in the second half of Statistical Inference by Casella & Berger.This said, the book is written well so that someone with a MS level background in Statistics can work through half the material on their own. In addition to this book, I’ve many side references and notes. Although this book is well written, and comprehensive, it does not hand hold you through the math so expect to need additional references. There is simply too much theory for the authors to be expected to work through much of the math (unfortunately). However, with the power of Google, you too can follow along the theory the book lays out for you.

⭐I am baffled by the reviews stating that the authors are clear and excellent writers. Reading this book is near torture, at least for anyone not already deeply immersed in probability distributions and their arcane nomenclature. Just a few of the problems include: 1) nearly constant use of new symbols without definition (unless you are already an expert, you will want Wikipedia at your elbow at least once per page for the first several chapters), 2) obscure and even baffling derivations and proofs that appear to require a PhD in math to follow (why include such a derivation in the main text?), 3) frequent inclusion of entire fields of endeavor without warning, justification, or introduction, 4) an impressively chaotic ordering of topics and examples, and 5) almost no attempt to provide an overview or sense of order for the topics. In addition to these widespread problems, the section on computer generation of random numbers is laughably out of date: as far as I could tell none of the algorithms and software mentioned are still in use in 2015, and none of the current commonly used methods are mentioned in this book. Presumably this just means that the book is getting a bit long in the tooth, but in a rapidly developing field that is important. I suspect this is a good reference book, and possibly it is even the best out there, but as a textbook it is a train wreck, even for advanced students.

⭐Well this book is our textbook. And there are a lot of mistakes and typos in the book. Sometimes it is misleading but it is a good book anyway

⭐If you want to understand the theory of MCMC, buy it. (If you also want to understand the theory of stochastic processes, buy Karlin and Taylor (both books – used – they are still the best – but be ready to work) and Parzen (also used)). Then buy “Introducing Monte Carlo Methods With R” (Robert and Casella) and “Bayesian Computation With R” (Albert) to understand how to do MCMC and what it means. Robert is (probably) the best statistician in Europe and one of the best in the world. He also writes extremely well. So does Albert.

⭐A comprehensive introduction to MCMC with mathematical details and lots of interesting and thought-provoking examples. The exercises are also very well designed and helpful for understanding the materials. I hope there are fewer typos in this book.

⭐It’s the best for statisticians .

⭐great!! thanks~

⭐Monte Carlo methods are old. They can be traced back to Buffon’s needle problem in the 17th century. However meaningful application had to wait for the invention of digital computers in the 20th century. Much of the development took place in the 1940s and 50s for military and nuclear engineering application. The Hastings – Metropolis algorithm of the 1950s has had a rebirth in the 1990s with the application of Markov Chain Monte Carlo methods to imaging problems and many Bayesian problems.The authors of this book are Bayesians and present Bayesian methods in the very first chapter. The book is intended to be a course text on Monte Carlo methods. I judge the level to be intermediate to advanced (first or second year graduate level). The first chapter introduces statistical and numerical problems that Monte Carlo methods can solve. It includes a discussion of bootstrap methods in the notes at the end of the chapter. Chapters 2 and 3 introduce standard topics including methods for generating pseudo-random numbers and various variance reduction techniques. Chapter 4 is an introduction to Markov Chains. Markov Chains are commonly a topic in introductory courses on stochastic processes. The authors presuppose that the reader has no knowledge of Markov Chains. So they develop the essential aspects of the theory needed in the application of Markov Chain Monte Carlo methods (MCMC). Chapter 5 then deals with optimization problems discussing simulated annealing, stochastic approximation and the EM algorithm. Chapters 6 – 8 deal with topic in MCMC methods. The final chapter deals with applications to missing data models. The topics are very current and important to statisticians. The theory is covered very well. Many interesting examples are provided throughout the book. A number of these are presented in the problems section at the end of the chapters. It also contains a very extensive bibliography.

⭐Il libro offre una panoramica esaustiva sul metodo MCMC, ed introduce elegantemente l’algoritmo Simulated Annealing. Inoltre, tratta gli aspetti teorici con accuratezza.

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⭐The order arrived very fast and has a very good quality, totally satisfied!

⭐El libro venía en un paquete muy inseguro, dentro de una especiae de caja que deja entrar polvo y demás sustancias que pueden maltratarlo. Traía unas marcas de huellas dactilares (pudieron evitar eso si lo empaquetaran correctamente).

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⭐The content is like a collection of papers. It is a very difficult read for a beginner in the field.

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