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A Markov chain is a sequence of events where the probability of each event is dependent on the event immediately preceding it, but independent of earlier events. Models and numerical equations are used to describe the patterns. This process is particularly useful in operations research and decision science for plotting the sequence of actions which will cause a system to perform optimally. This study provides a unified treatment of the theory, applications and computational methods for Markov decision processes. Important topics featured include action elimination methods, value iteration in the average reward case and sensitive discount optimality.
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1995 INFORMS Lanchester Prize Recipient
This book received the 1995 INFORMS Lanchester Prize as the best English language publication in operations research. I have used it in my course several times; a course outline is available on my web site. If you have any questions or comments about it please contact me.
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"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
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Book Description Wiley-Interscience, 1994. Hardcover. Condition: New. Never used!. Seller Inventory # P110471619779