Partially Observed Markov Decision Processes

Vikram Krishnamurthy

ISBN 10: 1107134609 / ISBN 13: 9781107134607

Published by Cambridge University Press, 2016

About this Item

: Este libro explora los procesos de decisión de Markov parcialmente observados, desde el filtrado hasta la detección controlada. Publicado en 2016 por Cambridge University Press, el libro tiene 488 páginas y está escrito en inglés. Es una obra académica que trata sobre procesos estocásticos y procesos de Markov. EAN: 9781107134607 Tipo: Libros Categoría: Ciencias|Tecnología|Educación Título: Partially Observed Markov Decision Processes Autor: Vikram Krishnamurthy Idioma: en Páginas: 488.

Seller Inventory # Happ-2026-08-04-1bf98c09

Bibliographic Details

• Title: Partially Observed Markov Decision Processes
• Publisher: Cambridge University Press
• Publication Date: 2016
• Language: English
• ISBN 10: 1107134609
• ISBN 13: 9781107134607
• Binding: Hardcover
• Condition: Muy bueno

About this title

Synopsis

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. Bringing together research from across the literature, the book provides an introduction to nonlinear filtering followed by a systematic development of stochastic dynamic programming, lattice programming and reinforcement learning for POMDPs. Questions addressed in the book include: when does a POMDP have a threshold optimal policy? When are myopic policies optimal? How do local and global decision makers interact in adaptive decision making in multi-agent social learning where there is herding and data incest? And how can sophisticated radars and sensors adapt their sensing in real time?

About the Author

Vikram Krishnamurthy is a Professor and Canada Research Chair in Statistical Signal Processing at the University of British Columbia, Vancouver. His research contributions focus on nonlinear filtering, stochastic approximation algorithms and POMDPs. Dr Krishnamurthy is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and served as a distinguished lecturer for the IEEE Signal Processing Society. In 2013, he received an honorary doctorate from KTH, Royal Institute of Technology, Sweden.