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Monte Carlo Statistical Methods (Springer Texts in Statistics)

3.9 out of 5 stars 11 customer reviews
ISBN-13: 978-1441919397
ISBN-10: 1441919392
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Frequently Bought Together

  • Monte Carlo Statistical Methods (Springer Texts in Statistics)
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  • Introducing Monte Carlo Methods with R (Use R!)
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Product Details

  • Series: Springer Texts in Statistics
  • Paperback: 649 pages
  • Publisher: Springer (November 30, 2010)
  • Language: English
  • ISBN-10: 1441919392
  • ISBN-13: 978-1441919397
  • Product Dimensions: 6.1 x 1.5 x 9.2 inches
  • Shipping Weight: 2.4 pounds (View shipping rates and policies)
  • Average Customer Review: 3.9 out of 5 stars  See all reviews (11 customer reviews)
  • Amazon Best Sellers Rank: #1,030,151 in Books (See Top 100 in Books)

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Customer Reviews

Top Customer Reviews

Format: Hardcover
This text may or may not be the best book on MC for a particular application; to be honest, it's the only book on MC I own.
However, I did peruse a number of texts before I bought this one, and I am very pleased with my decision. To me, this book does something that seems necessary but is relatively uncommon: it gives a detailed, modern, comprehensive introduction to MC methods per se. There are other texts that might have one of those characteristics, but they seem to either not have all of them: they either are not modern, not comprehensive, not introductory, or are not concerned with Monte Carlo per se.
Many other excellent texts, for example, are largely oriented toward Bayesian implementations, or general integration, but not both.
I would highly recommend this book as an excellent introduction to MC methods as a general computational tool.
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Format: Hardcover
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.
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Format: Hardcover
There is no doubts this text is a comprehensive study of Monte Carlo methods with an impressive number of examples. However, I must say it is hard to read for someone who is beginning to work with Monte Carlo methods. I highly recommend the book by Sobol (A primer for the Monte Carlo Method) which I think it remains to be the best introduction to the subject. After reading and enjoying this primer you will be ready to take full advantage of Robert and Casella's book.
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Format: Paperback Verified Purchase
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.
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Format: Hardcover Verified Purchase
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.
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