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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Partially Observed Markov Decision Processes: From Filtering to Controlled Sensing

Partially Observed Markov Decision Processes: From Filtering to Controlled Sensing

Krishnamurthy, Vikram

Hardcover

Technology & EngineeringGeneral MathematicsProbability & Statistics

Currently unavailable to order

ISBN10: 1107134609
ISBN13: 9781107134607
Publisher: Cambridge University Press
Published: Mar 21 2016
Pages: 488
Weight: 2.53
Height: 1.19 Width: 6.90 Depth: 10.12
Language: English
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?

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