Partially observed Markov decision processes : (Record no. 84259)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02428nam a2200337 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | CR9781316471104 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730160808.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 150526s2016||||enk o ||1 0|eng|d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781316471104 (ebook) |
| 082 00 - CLASSIFICATION NUMBER | |
| Call Number | 519.2/33 |
| 100 1# - AUTHOR NAME | |
| Author | Krishnamurthy, V. |
| 245 10 - TITLE STATEMENT | |
| Title | Partially observed Markov decision processes : |
| Sub Title | from filtering to controlled sensing / |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 online resource (xiii, 476 pages) : |
| 500 ## - GENERAL NOTE | |
| Remark 1 | Title from publisher's bibliographic system (viewed on 05 Apr 2016). |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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? |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1017/CBO9781316471104 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cambridge : |
| -- | Cambridge University Press, |
| -- | 2016. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Markov processes |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Stochastic processes |
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