000 03622nam a22005535i 4500
001 978-3-031-79289-2
003 DE-He213
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008 220601s2020 sz | s |||| 0|eng d
020 _a9783031792892
_9978-3-031-79289-2
024 7 _a10.1007/978-3-031-79289-2
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aZhao, Qing.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983493
245 1 0 _aMulti-Armed Bandits
_h[electronic resource] :
_bTheory and Applications to Online Learning in Networks /
_cby Qing Zhao.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXVIII, 147 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aArtificial intelligence.
_93407
650 0 _aCooperating objects (Computer systems).
_96195
650 0 _aProgramming languages (Electronic computers).
_97503
650 0 _aTelecommunication.
_910437
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aCyber-Physical Systems.
_932475
650 2 4 _aProgramming Language.
_939403
650 2 4 _aCommunications Engineering, Networks.
_931570
710 2 _aSpringerLink (Online service)
_983498
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031792908
776 0 8 _iPrinted edition:
_z9783031792885
776 0 8 _iPrinted edition:
_z9783031792915
830 0 _aSynthesis Lectures on Learning, Networks, and Algorithms,
_x2690-4314
_983499
856 4 0 _uhttps://doi.org/10.1007/978-3-031-79289-2
912 _aZDB-2-SXSC
942 _cEBK
999 _c85519
_d85519