Monte Carlo Statistical Methods (Springer Texts in Statistics)
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)