Partially Observed Markov Decision Processes : Filtering, Learning and Controlled Sensing

2 Revised edition

by Vikram Krishnamurthy

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

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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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