Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics)
Markov Decision Processes: Discrete Stochastic Dynamic Programming offers a rigorous exploration of stochastic dynamic programming and Markov decision processes. Combining ideas from probability, statistics, mathematics, and optimization, Martin L. Puterman’s book provides a focused reference for understanding sequential decision-making in discrete stochastic settings.
About This Book
Markov Decision Processes: Discrete Stochastic Dynamic Programming presents a focused study of Markov decision processes and their role in stochastic dynamic programming.
The subject combines probability, statistics, optimization, and sequential decision-making in a mathematical framework for analyzing decisions made over time.
As part of the Wiley Series in Probability and Statistics, the book is suited to readers seeking a rigorous reference in these interconnected fields.
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