Multi-Armed Bandits (Record no. 85519)

000 -LEADER
fixed length control field 03622nam a22005535i 4500
001 - CONTROL NUMBER
control field 978-3-031-79289-2
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730164258.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220601s2020 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031792892
-- 978-3-031-79289-2
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
100 1# - AUTHOR NAME
Author Zhao, Qing.
245 10 - TITLE STATEMENT
Title Multi-Armed Bandits
Sub Title Theory and Applications to Online Learning in Networks /
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2020.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XVIII, 147 p.
490 1# - SERIES STATEMENT
Series statement Synthesis Lectures on Learning, Networks, and Algorithms,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Preface -- 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 ## - SUMMARY, ETC.
Summary, etc Multi-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.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-79289-2
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2020.
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-- txt
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-- computer
-- c
-- rdamedia
338 ## -
-- online resource
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347 ## -
-- text file
-- PDF
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Cooperating objects (Computer systems).
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Programming languages (Electronic computers).
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-- Telecommunication.
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-- Artificial Intelligence.
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-- Cyber-Physical Systems.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Programming Language.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Communications Engineering, Networks.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2690-4314
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-- ZDB-2-SXSC

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