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Markov Chains and Stochastic Stability (Communications and Control Engineering) Hardcover – April 26, 1996

ISBN-13: 978-3540198321 ISBN-10: 3540198326 Edition: 1st

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Product Details

  • Series: Communications and Control Engineering
  • Hardcover: 550 pages
  • Publisher: Springer; 1 edition (April 26, 1996)
  • Language: English
  • ISBN-10: 3540198326
  • ISBN-13: 978-3540198321
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #11,131,488 in Books (See Top 100 in Books)

Editorial Reviews


"As Glynn puts it in his prologue, "This second edition remains true to the remarkable standards of scholarship established by the first edition... This new edition does a splendid job of making clear the most important [new] developments and pointing the reader in the direction of key references to be studied in each area." The reviewer fully agrees with this assessment."
M. Iosifescu, Mathematical Reviews

"The second edition of Meyn and Tweedie's Markov Chains and Stochastic Stability is out. This is great news. If you do not have this book yet, you should hurry up and get yourself a copy at a very reasonable price, and if you do own a copy already, it is probably falling apart by now from frequent use, so upgrade to the second edition."
Gennady Samorodnitsky, Journal of the American Statistical Association --This text refers to the Paperback edition.

Book Description

New up-to-date edition of this influential classic on Markov chains in general state spaces. Proofs are rigorous and concise, the range of applications is broad and knowledgeable, and key ideas are accessible to practitioners with limited mathematical background. New commentary by Sean Meyn, including updated references, reflects developments since 1996. --This text refers to the Paperback edition.

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Most Helpful Customer Reviews

8 of 8 people found the following review helpful By Random Thoughts on December 23, 2009
Format: Paperback Verified Purchase
It is certainly great news for researchers working with Markov chains that this widely used book got reprinted with a new publisher. The content is almost the same as the first version, except for some notes and bibilographic updates by the second author and a nice foreward by Peter Glynn. Of course, sadly the first author is no longer with us today, and the second author has done a good job of putting a modern touch to the book. I think Markov chain theory is still of interest today for at least two reasons. First, Markov models seem to have more and more applications everyday, from modern cummunication networks to molecular biological data analysis, and so it pays to have a grasp and some understanding of the basic properties of concrete models, whether being stable, or being sensitive to parameter perturbations. This book provides a good introduction and foundation for understanding stochastic dynamical systems. Secondly, there is an intrinsic need in statistical theory for Markov chain model, as it is perhaps the simplest and most natural model for dependence in data, generalizing standard evolution equations such as ODE or PDE models in the sciences literature. For example, both time series analysis and Bayesian statistical computation make heavy use of Markov chain theory. I think this book should be taught at the graduate level at most major statistics departments. This book makes an interesting comparison to another classic book on this subject: E. Nummelin's bookGeneral Irreducible Markov Chains and Non-Negative Operators (Cambridge Tracts in Mathematics) which is, often, overlooked and under-appreciated.Read more ›
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