Partially Observed Markov Decision Processes Filtering, Learning and Controlled Sensing
Partially Observed Markov Decision Processes Filtering, Learning and Controlled Sensing by Vikram Krishnamurthy is a comprehensive resource published by Cambridge University Press on June 5, 2025. This 651-page book is presented in English and delves into the formulation, algorithms, and structural results of partially observed Markov decision processes (POMDPs), linking theoretical concepts to real-world applications in controlled sensing.
Readers will find a focus on the conceptual foundations of POMDPs, with an emphasis on structural results in stochastic dynamic programming. This edition introduces a new Part V on inverse reinforcement learning and includes a chapter on non-parametric Bayesian inference, covering topics such as Dirichlet processes and Gaussian processes, variational Bayes, and conformal prediction. The content is designed to aid graduate students and researchers in fields such as engineering, operations research, and economics, facilitating an understanding of key themes without excessive mathematical complexity.
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Covering formulation, algorithms and structural results and linking theory to real-world applications in controlled sensing (including social learning, adaptive radars and sequential detection), this book focuses on the conceptual foundations of partially observed Markov decision processes (POMDPs). It emphasizes structural results in stochastic dynamic programming, enabling graduate students and researchers in engineering, operations research, and economics to understand the underlying unifying themes without getting weighed down by mathematical technicalities. In light of major advances in machine learning over the past decade, this edition includes a new Part V on inverse reinforcement learning as well as a new chapter on non-parametric Bayesian inference (for Dirichlet processes and Gaussian processes), variational Bayes and conformal prediction.
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