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Markov Decision Processes: Discrete Stochastic Dynamic Programming By Martin L. Puterman
Download Markov Decision Processes: Discrete Stochastic Dynamic Programming By Martin L. Puterman
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- Sales Rank: #874878 in Books
- Published on: 2005-03-03
- Original language: English
- Number of items: 1
- Dimensions: 9.20" h x 1.40" w x 6.10" l, 2.07 pounds
- Binding: Paperback
- 684 pages
From the Publisher
An up-to-date, unified and rigorous treatment of theoretical, computational and applied research on Markov decision process models. Concentrates on infinite-horizon discrete-time models. Discusses arbitrary state spaces, finite-horizon and continuous-time discrete-state models. Also covers modified policy iteration, multichain models with average reward criterion and sensitive optimality. Features a wealth of figures which illustrate examples and an extensive bibliography.
From the Back Cover
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.
"This text is unique in bringing together so many results hitherto found only in part in other texts and papers. . . . The text is fairly self-contained, inclusive of some basic mathematical results needed, and provides a rich diet of examples, applications, and exercises. The bibliographical material at the end of each chapter is excellent, not only from a historical perspective, but because it is valuable for researchers in acquiring a good perspective of the MDP research potential."
--Zentralblatt fur Mathematik
." . . it is of great value to advanced-level students, researchers, and professional practitioners of this field to have now a complete volume (with more than 600 pages) devoted to this topic. . . . Markov Decision Processes: Discrete Stochastic Dynamic Programming represents an up-to-date, unified, and rigorous treatment of theoretical and computational aspects of discrete-time Markov decision processes."
--Journal of the American Statistical Association
About the Author
Martin L. Puterman, PhD, is Advisory Board Professor of Operations and Director of the Centre for Operations Excellence at The University of British Columbia in Vancouver, Canada.
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