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007			cr nn 008mamaa
008			220601s2020 sz | o |||| 0|eng d
020			_a9783031792892
024	7		_a10.1007/978-3-031-79289-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2020 EB
100	1		_aZhao, Qing _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688308
245	1	0	_aMulti-Armed Bandits : _bTheory and Applications to Online Learning in Networks _cby Qing Zhao
250			_a1st edition 2020
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XVIII, 147 páginas)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Learning Networks and Algorithms _x2690-4314
505	0		_aPreface -- Acknowledgments -- Introduction -- Bayesian Bandit Model and Gittins Index -- Variants of the Bayesian Bandit Model -- Frequentist Bandit Model -- Variants of the Frequentist Bandit Model -- Application Examples -- Bibliography -- Author's Biography.
520			_aMulti-armed bandit problems pertain to optimal sequential decision making and learning in unknown environments. Since the first bandit problem posed by Thompson in 1933 for the application of clinical trials, bandit problems have enjoyed lasting attention from multiple research communities and have found a wide range of applications across diverse domains. This book covers classic results and recent development on both Bayesian and frequentist bandit problems. We start in Chapter 1 with a brief overview on the history of bandit problems, contrasting the two schools-Bayesian and frequentist-of approaches and highlighting foundational results and key applications. Chapters 2 and 4 cover, respectively, the canonical Bayesian and frequentist bandit models. In Chapters 3 and 5, we discuss major variants of the canonical bandit models that lead to new directions, bring in new techniques, and broaden the applications of this classical problem. In Chapter 6, we present several representative application examples in communication networks and social-economic systems, aiming to illuminate the connections between the Bayesian and the frequentist formulations of bandit problems and how structural results pertaining to one may be leveraged to obtain solutions under the other.
988			_aSynthesis Collection of Technology_2020
650		7	_2embne _9166090 _aAprendizaje automático
776	0	8	_iPrinted edition: _z9783031792908
776	0	8	_iPrinted edition: _z9783031792885
776	0	8	_iPrinted edition: _z9783031792915
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-79289-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _eb _zSI