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＜電子ブック＞
Continuous-Time Markov Decision Processes : Theory and Applications / by Xianping Guo, Onésimo Hernández-Lerma
(Stochastic Modelling and Applied Probability. ISSN:2197439X ; 62)

版	1st ed. 2009.
出版者	(Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer)
出版年	2009
本文言語	英語
大きさ	XVIII, 234 p : online resource
著者標目	*Guo, Xianping author Hernández-Lerma, Onésimo author SpringerLink (Online service)
件　名	LCSH:Mathematical optimization LCSH:Probabilities LCSH:Operations research LCSH:Management science FREE:Optimization FREE:Probability Theory FREE:Operations Research, Management Science
一般注記	and Summary -- Continuous-Time Markov Decision Processes -- Average Optimality for Finite Models -- Discount Optimality for Nonnegative Costs -- Average Optimality for Nonnegative Costs -- Discount Optimality for Unbounded Rewards -- Average Optimality for Unbounded Rewards -- Average Optimality for Pathwise Rewards -- Advanced Optimality Criteria -- Variance Minimization -- Constrained Optimality for Discount Criteria -- Constrained Optimality for Average Criteria Continuous-time Markov decision processes (MDPs), also known as controlled Markov chains, are used for modeling decision-making problems that arise in operations research (for instance, inventory, manufacturing, and queueing systems), computer science, communications engineering, control of populations (such as fisheries and epidemics), and management science, among many other fields. This volume provides a unified, systematic, self-contained presentation of recent developments on the theory and applications of continuous-time MDPs. The MDPs in this volume include most of the cases that arise in applications, because they allow unbounded transition and reward/cost rates. Much of the material appears for the first time in book form HTTP:URL=https://doi.org/10.1007/978-3-642-02547-1

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