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

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.

Product details

• Authors: Vikram (Cornell University Krishnamurthy, Krishnamurthy Vikram
• Publisher: Cambridge University Press ELT
• Languages: English
• Product format: Hardback
• Release: 31.05.2025
• EAN: 9781009449434
• ISBN: 978-1-0-0944943-4
• No. of pages: 651
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Social sciences, law, business > Media, communication > Communication science; machine learning, Artificial Intelligence, MATHEMATICS / Probability & Statistics / General, pattern recognition, Probability & statistics, Stochastics, Information theory, Computer networking & communications, Probability and statistics, COMPUTERS / Data Science / Machine Learning, Computer networking and communications