Monte Carlo Strategies in Scientific Computing and over one million other books are available for Amazon Kindle. Learn more
Trade in your item
Get a $33.56
Gift Card.
Have one to sell? Sell on Amazon
Flip to back Flip to front
Listen Playing... Paused   You're listening to a sample of the Audible audio edition.
Learn more
See this image

Monte Carlo Strategies in Scientific Computing (Springer Series in Statistics) Hardcover – January 4, 2008

ISBN-13: 978-0387952307 ISBN-10: 0387952306 Edition: Corrected

5 New from $440.34 10 Used from $128.98
Amazon Price New from Used from
"Please retry"
"Please retry"
$440.34 $128.98



Save up to 90% on Textbooks
Rent textbooks, buy textbooks, or get up to 80% back when you sell us your books. Shop Now

Product Details

  • Series: Springer Series in Statistics
  • Hardcover: 346 pages
  • Publisher: Springer; Corrected edition (January 4, 2008)
  • Language: English
  • ISBN-10: 0387952306
  • ISBN-13: 978-0387952307
  • Product Dimensions: 0.8 x 6.3 x 9.8 inches
  • Shipping Weight: 1.3 pounds
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (4 customer reviews)
  • Amazon Best Sellers Rank: #2,053,606 in Books (See Top 100 in Books)

Editorial Reviews


From the reviews:


"This book is an excellent survey of current Monte Carlo methods. A strength of the book is the inclusion of a number of applications to current scientific problems. The applications amply demonstrate the relevance of this approach to modern computing. There is a fairly thorough coverage of wide variety of Monte Carlo algorithms that have arisen in diverse fields such as physics, chemistry, biology, etc., and the relationship among them. The book is highly recommended."


"This is a worthwhile reference to recent advances in sequential Monte Carlo, primarily Bayesian and Markov Chain methods. To those with an interest in these topics, it is worth a read."

"This well written book discusses why Monte Carlo techniques are needed, the importance of Monte Carlo in bioinformatics, target tracking in nonlinear dynamic systems, in missing data analysis … . The references are exhaustive. I enjoyed reading this book and learned a lot about the genetic applications of Monte Carlo techniques. I recommend this book highly to statisticians and geneticists." (Ramalingam Shanmugam, Journal of Statistical Computation and Simulation, Vol. 74 (8), 2004)

"Markov chain Monte Carlo … was introduced to tackle more sophisticated and realistic statistical models as in the Bayesian approach of statistics. The author is well known in the area of MCMC methods … . The book is written in a proper style … . It provides an actual view of theoretical developments complemented by applications … . It may be highly recommended for scientists and graduate students who want to gain some insight in either the theory or application of advanced Monte Carlo methods." (Ernst Stadlober, Metrika, February, 2004)

"This book provides comprehensive coverage of Monte Carlo methods, and in the process uncovers and discusses commonalities among seemingly disparate techniques that arose in various areas of application. … The book is well organized; the flow of topics follows a logical development. … The coverage is up-to-date and comprehensive, and so the book is a good resource for people conducting research on Monte Carlo methods. … The book would be an excellent supplementary text for a course in scientific computing … ." (James E. Gentle, SIAM Review, Vol. 44 (3), 2002)

"The strength of this book is in bringing together advanced Monte Carlo (MC) methods developed in many disciplines. … Throughout the book are examples of techniques invented, or reinvented, in different fields that may be applied elsewhere. … Monte Carlo Strategies in Scientific Computing offers a large … variety of methods and examples. Those interested in using MC to solve difficult problems will find many ideas, collected from a variety of disciplines, and references for further study." (Tim Hesterberg, Technometrics, Vol. 44 (4), 2002)

"This recent addition to the Monte Carlo literature is divided into 13 chapters and an appendix. It provides both the methodology and the underlying theory for applying Monte Carlo techniques to a broad range of problems. … In the Appendix the author outlines the basics in probability theory and statistical inference procedures. … this book is a valuable and recommended reference to Monte Carlo methods; particularly it draws the attention to recent work in sequential Monte Carlo." (Radu Theodorescu, Zentralblatt MATH, Vol. 991, 2002)

"The book gives a good introduction to current Monte Carlo methods and explains the terminology on a moderate level of abstraction. It becomes clear that any specific problem needs a tailored algorithm to be efficient. This is the reason for the emergence of variance reduction methods, importance sampling, rejection, sequential MC, Metropolis algorithms, Gibbs samplers, Markov Chain MC (MCMC), or hybrid MC with molecular dynamics. … it is one of the first attempts to show the general principles behind an apparent zoo of methods." (W. Wiechert, Simulation News Europe, Issue 34, 2002)

"The book targets a broader topic, namely all Monte Carlo methods. … No prior MCMC knowledge is assumed, and the topics are introduced and motivated along the way. … The book mentions plenty of real life situations where the techniques discussed … may be applied. … this book is sure to help the aspiring student eager to peep into the world of Monte Carlo. At the same time its extensive bibliography and references will make it useful as a handbook for the more advanced researcher." (Arnab Chakraborty, Sankhya: Indian Journal of Statistics, Vol. 64 (1B), 2002)

More About the Author

Discover books, learn about writers, read author blogs, and more.

Customer Reviews

4.5 out of 5 stars
5 star
4 star
3 star
2 star
1 star
See all 4 customer reviews
Share your thoughts with other customers

Most Helpful Customer Reviews

16 of 18 people found the following review helpful By Reader in Statistics on May 3, 2006
Format: Hardcover
Jun Liu has been a prominent researcher in MCMC since the mid 90's. His research has contributed a great deal to the development of Gibbs sampler, sequential Monte Carlo, weighting/importance sampling, missing data, and MCMC related applications in Bioinformatics. Not surprisingly, this book has them all, plus many other interesting topics. The final two chapters review some of the theories. This book has a strong flavor in statistical physics, which I like very much. It also contains some applications in, for examples, engineering (e.g. nonlinear filter, sequential Monte Carlo), biology (DNA sequencing), image analysis (clustering) and stochastic optimization.

Jun Liu presents things very clearly and concisely, and hopefully you can benefit from his book.
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again
33 of 48 people found the following review helpful By A Customer on August 6, 2001
Format: Hardcover
The author is a top young gun from Harvard's Statistics Dept., and is an expert in many applied areas that utilize Monte Carlo, like the red hot bioinformatics. This book covers MC techniques developed in many different fields e.g., physics,structural biology, statistics. It has a wide range of examples, some of which are very new (e.g., bioinformatics) and non-standard. It contains many interesting ideas, and is concise mathematically and easy to read. Highly recommended.
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again
7 of 16 people found the following review helpful By Jingru Chen on August 21, 2005
Format: Hardcover
Solid theory in Monte Carlo, but less application examples
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again
3 of 13 people found the following review helpful By supercutepig on September 12, 2005
Format: Hardcover
Now, I am reading this book. I would like to mark it 4.5 stars if possible.

[1] The author is an expert of computational statistics and Bayesian analysis, an active mathematician at Harvard.

[2] The background of this book is related to bioinformatics, physics, etc, which puzzles me a lot while reading.

[3] You can find the author's deep understanding of MC methods throughout the book.

[3] It is suitable for the graduate students of statistics.

[4] It's a little bit pity that this book is not purely written for mathematicians. Anyway, it is a witness of MC methods in development.
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again

Customer Images