| 書目名稱 | Discrete-Time Markov Control Processes | | 副標(biāo)題 | Basic Optimality Cri | | 編輯 | Onésimo Hernández-Lerma,Jean Bernard Lasserre | | 視頻video | http://file.papertrans.cn/282/281198/281198.mp4 | | 叢書名稱 | Stochastic Modelling and Applied Probability | | 圖書封面 |  | | 描述 | This book presents the first part of a planned two-volume series devoted to a systematic exposition of some recent developments in the theory of discrete-time Markov control processes (MCPs). Interest is mainly confined to MCPs with Borel state and control (or action) spaces, and possibly unbounded costs and noncompact control constraint sets. MCPs are a class of stochastic control problems, also known as Markov decision processes, controlled Markov processes, or stochastic dynamic pro- grams; sometimes, particularly when the state space is a countable set, they are also called Markov decision (or controlled Markov) chains. Regardless of the name used, MCPs appear in many fields, for example, engineering, economics, operations research, statistics, renewable and nonrenewable re- source management, (control of) epidemics, etc. However, most of the lit- erature (say, at least 90%) is concentrated on MCPs for which (a) the state space is a countable set, and/or (b) the costs-per-stage are bounded, and/or (c) the control constraint sets are compact. But curiously enough, the most widely used control model in engineering and economics--namely the LQ (Linear system/Quadratic cost) model- | | 出版日期 | Book 1996 | | 關(guān)鍵詞 | Markov property; linear optimization; management; model; operations research; production; programming; qual | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4612-0729-0 | | isbn_softcover | 978-1-4612-6884-0 | | isbn_ebook | 978-1-4612-0729-0Series ISSN 0172-4568 Series E-ISSN 2197-439X | | issn_series | 0172-4568 | | copyright | Springer Science+Business Media New York 1996 |
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