Partially Observed Markov Decision Processes : Filtering, Learning and Controlled Sensing
2 Revised edition
Book Details
Format
Hardback or Cased Book
ISBN-10
1009449435
ISBN-13
9781009449434
Edition
2 Revised edition
Publisher
Cambridge University Press
Imprint
Cambridge University Press
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Jun 5th, 2025
Print length
651 Pages
Weight
1,384 grams
Dimensions
18.60 x 26.10 x 4.30 cms
Ksh 18,600.00
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This survey of formulation, algorithms, and structural results in POMDPs focuses on underlying concepts and connections to real-world applications in controlled sensing, keeping technical machinery to a minimum. The new edition includes inverse reinforcement learning, non-parametric Bayesian inference, variational Bayes and conformal prediction.
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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