- Paperback: 264 pages
- Publisher: Taylor & Francis Ltd (1 Oct 1997)
- Language English
- ISBN-10: 0412818205
- ISBN-13: 978-0412818202
- Product Dimensions: 24 x 15.1 x 1.4 cm
- Average Customer Review: 4.0 out of 5 stars See all reviews (1 customer review)
- Amazon Bestsellers Rank: 3,309,661 in Books (See Top 100 in Books)
- See Complete Table of Contents
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Product Description
Product Description
Bridging the gap between research and application, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference provides a concise, and integrated account of Markov chain Monte Carlo (MCMC) for performing Bayesian inference. This volume, which was developed from a short course taught by the author at a meeting of Brazilian statisticians and probabilists, retains the didactic character of the original course text. The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. It describes each component of the theory in detail and outlines related software, which is of particular benefit to applied scientists.
Inside This Book
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First Sentence
The word simulation refers to the treatment of a real problem through reproduction in an environment controlled by the experimenter. Read the first page
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
The word simulation refers to the treatment of a real problem through reproduction in an environment controlled by the experimenter. Read the first page
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Browse Sample PagesConcordance
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Customer Reviews
Most Helpful Customer Reviews
5 of 5 people found the following review helpful
Format:Paperback
Markov chain Monte Carlo Methods are bringing a revolution into statistics, and in particular in Bayesian statistics. This book is a great introduction to these methods: it is clear, not too formal, easily readable for anybody, who has taken an undergraduate class in mathematical statistics. Applications to a wide range of
statistical techniques are presented (hierarchical models, dynamic models, generalized linear models).
The right book to start with, to understand and to be quickly operative with MCMC methods.
statistical techniques are presented (hierarchical models, dynamic models, generalized linear models).
The right book to start with, to understand and to be quickly operative with MCMC methods.
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com:
3 reviews
2 of 2 people found the following review helpful
A relevant contribution to understand MCMC simulation
3 Dec 2011
Format:Hardcover
If you are a Bayesian statistician or maybe if you want to understand this statistical paradigm, this book is not only necessary but indispensable. Professor Gamerman and Lopes present an excellent study about the simulation techniques that we need to implement if we want to draw samples from posterior distribution when we don't have this in a closed way. This book brilliantly analyzes the most basic MCMC methods like Gibbs Sampler and Metropolis Hastings, on the other hand the reader shall find a lot of funny examples and some applications.
Marlov Chain Monte Carlo Techniques
23 May 2012
Format:Paperback|Amazon Verified Purchase
This is an excellent book. It describes the material clearly, using plenty of cogent examples. I gladly recccomend this book.
11 of 20 people found the following review helpful
very good
14 Feb 2009
Format:Hardcover|Amazon Verified Purchase
This book is very self-contained and provides intuitive explanations and illuminating examples. Very good for self-study.
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