Discrete-Time Markov Control Processes: Basic Optimality Criteria
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
1996
|
| Schriftenreihe: | Applications of Mathematics, Stochastic Modelling and Applied Probability
30 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | 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-satisfies none of these conditions. Moreover, when dealing with "partially observable" systems) a standard approach is to transform them into equivalent "completely observable" sys tems in a larger state space (in fact, a space of probability measures), which is uncountable even if the original state process is finite-valued |
| Beschreibung: | 1 Online-Ressource (XIV, 216 p) |
| ISBN: | 9781461207290 9781461268840 |
| ISSN: | 0172-4568 |
| DOI: | 10.1007/978-1-4612-0729-0 |
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Datensatz im Suchindex
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| author | Hernández-Lerma, Onésimo |
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| discipline | Mathematik |
| doi_str_mv | 10.1007/978-1-4612-0729-0 |
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| isbn | 9781461207290 9781461268840 |
| issn | 0172-4568 |
| language | English |
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| series2 | Applications of Mathematics, Stochastic Modelling and Applied Probability |
| spelling | Hernández-Lerma, Onésimo Verfasser aut Discrete-Time Markov Control Processes Basic Optimality Criteria by Onésimo Hernández-Lerma, Jean Bernard Lasserre New York, NY Springer New York 1996 1 Online-Ressource (XIV, 216 p) txt rdacontent c rdamedia cr rdacarrier Applications of Mathematics, Stochastic Modelling and Applied Probability 30 0172-4568 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-satisfies none of these conditions. Moreover, when dealing with "partially observable" systems) a standard approach is to transform them into equivalent "completely observable" sys tems in a larger state space (in fact, a space of probability measures), which is uncountable even if the original state process is finite-valued Mathematics Distribution (Probability theory) Probability Theory and Stochastic Processes Mathematik Diskreter Markov-Prozess (DE-588)4150185-8 gnd rswk-swf Markov-Entscheidungsprozess (DE-588)4168927-6 gnd rswk-swf Markov-Entscheidungsprozess (DE-588)4168927-6 s Diskreter Markov-Prozess (DE-588)4150185-8 s 1\p DE-604 Lasserre, Jean Bernard Sonstige oth https://doi.org/10.1007/978-1-4612-0729-0 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Hernández-Lerma, Onésimo Discrete-Time Markov Control Processes Basic Optimality Criteria Mathematics Distribution (Probability theory) Probability Theory and Stochastic Processes Mathematik Diskreter Markov-Prozess (DE-588)4150185-8 gnd Markov-Entscheidungsprozess (DE-588)4168927-6 gnd |
| subject_GND | (DE-588)4150185-8 (DE-588)4168927-6 |
| title | Discrete-Time Markov Control Processes Basic Optimality Criteria |
| title_auth | Discrete-Time Markov Control Processes Basic Optimality Criteria |
| title_exact_search | Discrete-Time Markov Control Processes Basic Optimality Criteria |
| title_full | Discrete-Time Markov Control Processes Basic Optimality Criteria by Onésimo Hernández-Lerma, Jean Bernard Lasserre |
| title_fullStr | Discrete-Time Markov Control Processes Basic Optimality Criteria by Onésimo Hernández-Lerma, Jean Bernard Lasserre |
| title_full_unstemmed | Discrete-Time Markov Control Processes Basic Optimality Criteria by Onésimo Hernández-Lerma, Jean Bernard Lasserre |
| title_short | Discrete-Time Markov Control Processes |
| title_sort | discrete time markov control processes basic optimality criteria |
| title_sub | Basic Optimality Criteria |
| topic | Mathematics Distribution (Probability theory) Probability Theory and Stochastic Processes Mathematik Diskreter Markov-Prozess (DE-588)4150185-8 gnd Markov-Entscheidungsprozess (DE-588)4168927-6 gnd |
| topic_facet | Mathematics Distribution (Probability theory) Probability Theory and Stochastic Processes Mathematik Diskreter Markov-Prozess Markov-Entscheidungsprozess |
| url | https://doi.org/10.1007/978-1-4612-0729-0 |
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