000			02428nam a2200337 i 4500
001			CR9781316471104
003			UkCbUP
005			20240730160808.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			150526s2016||||enk o ||1 0|eng|d
020			_a9781316471104 (ebook)
020			_z9781107134607 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA274.7 _b.K75 2016
082	0	0	_a519.2/33 _223
100	1		_aKrishnamurthy, V. _q(Vikram), _eauthor. _928338
245	1	0	_aPartially observed Markov decision processes : _bfrom filtering to controlled sensing / _cVikram Krishnamurthy, University of British Columbia, Vancouver, Canada.
264		1	_aCambridge : _bCambridge University Press, _c2016.
300			_a1 online resource (xiii, 476 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Apr 2016).
520			_aCovering 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?
650		0	_aMarkov processes _vTextbooks. _974824
650		0	_aStochastic processes _vTextbooks. _94443
776	0	8	_iPrint version: _z9781107134607
856	4	0	_uhttps://doi.org/10.1017/CBO9781316471104
942			_cEBK
999			_c84259 _d84259