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Selected topics on continuous-time controlled Markov chains and Markov games.
This book concerns continuous-time controlled Markov chains, also known as continuous-time Markov decision processes. They form a class of stochastic control problems in which a single decision-maker wishes to optimize a given objective function. This book is also concerned with Markov games, where...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London : Singapore ; Hackensack, NJ :
Imperial College Press ; Distributed by World Scientific Publishing Co.,
©2012.
|
| Colección: | Imperial College Press advanced texts in mathematics ;
v. 5. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Prieto-Rumeau, Tomás. | |
| 245 | 1 | 0 | |a Selected topics on continuous-time controlled Markov chains and Markov games. |
| 260 | |a London : |b Imperial College Press ; |a Singapore ; |a Hackensack, NJ : |b Distributed by World Scientific Publishing Co., |c ©2012. | ||
| 300 | |a 1 online resource | ||
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| 490 | 1 | |a ICP advanced texts in mathematics ; |v v. 5 | |
| 588 | 0 | |a Online resource; title from digital title page (viewed May 24, 2012). | |
| 520 | |a This book concerns continuous-time controlled Markov chains, also known as continuous-time Markov decision processes. They form a class of stochastic control problems in which a single decision-maker wishes to optimize a given objective function. This book is also concerned with Markov games, where two decision-makers (or players) try to optimize their own objective function. Both decision-making processes appear in a large number of applications in economics, operations research, engineering, and computer science, among other areas. An extensive, self-contained, up-to-date analysis of basic o. | ||
| 505 | 0 | |a Preface; Contents; 1. Introduction; 1.1 Preliminary examples; 1.1.1 A controlled population system; 1.1.2 A prey-predator game model; 1.2 Overview of the book; 1.3 Contents; 1.4 Notation; 2. Controlled Markov Chains; 2.1 Introduction; 2.2 The control model; 2.3 Existence of controlled Markov chains; 2.4 Exponential ergodicity; 2.5 Proof of Theorem 2.11; 2.6 Conclusions; 3. Basic Optimality Criteria; 3.1 Introduction; 3.2 The finite horizon case; 3.3 The infinite horizon discounted reward; 3.3.1 Definitions; 3.3.2 The discounted reward optimality equation; 3.3.3 The uniformization technique. | |
| 504 | |a Includes bibliographical references (pages 265-274) and index. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Markov processes. | |
| 650 | 0 | |a Stochastic processes. | |
| 650 | 2 | |a Markov Chains | |
| 650 | 2 | |a Stochastic Processes | |
| 650 | 6 | |a Processus de Markov. | |
| 650 | 6 | |a Processus stochastiques. | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x Stochastic Processes. |2 bisacsh | |
| 650 | 7 | |a Markov processes |2 fast | |
| 650 | 7 | |a Stochastic processes |2 fast | |
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