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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.
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Discrete-Time Markov Control Processes: Basic Optimality Criteria (Applications of Mathematics, Volume 30)
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Springer, 1996
ISBN 10 : 0387945792
ISBN 13 : 9780387945798
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Hardcover. Etat : Très bon. Ancien livre de bibliothèque avec équipements. Edition 1996. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 1996. Ammareal gives back up to 15% of this item's net price to charity organizations. N° de réf. du vendeur G-312-075
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Discrete-time Markov control processes basic optimality criteria Onésimo Hernández-Lerma, Jean Bernard Lasserre. Applications of mathematics 30.
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New York, Springer [1996]., 1996
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Hardcover. Ex-library with stamp and library-signature. GOOD condition, some traces of use. Ancien Exemplaire de bibliothèque avec signature et cachet. BON état, quelques traces d'usure. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. 93 HER 9780387945798 Sprache: Englisch Gewicht in Gramm: 550. N° de réf. du vendeur 2508985
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Discrete-Time Markov Control Processes : Basic Optimality Criteria
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Springer New York, 1995
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ISBN 13 : 9780387945798
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Discrete-Time Markov Control Processes
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Springer New York, 1995
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 . N° de réf. du vendeur 5912091
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Discrete-Time Markov Control Processes: Basic Optimality Criteria (Applications of Mathematics, Volume 30)
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Discrete-Time Markov Control Processes
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Springer New York Dez 1995, 1995
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ISBN 13 : 9780387945798
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 236 pp. Englisch. N° de réf. du vendeur 9780387945798
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Discrete-Time Markov Control Processes
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Springer New York, Springer New York Dez 1995, 1995
ISBN 10 : 0387945792
ISBN 13 : 9780387945798
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Buch. Etat : Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 236 pp. Englisch. N° de réf. du vendeur 9780387945798
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Discrete-Time Markov Control Processes : Basic Optimality Criteria
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Springer New York, Springer New York, 1996
ISBN 10 : 0387945792
ISBN 13 : 9780387945798
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N° de réf. du vendeur 9780387945798
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Discrete-Time Markov Control Processes: Basic Optimality Criteria
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Springer-Verlag New York Inc., 1996
ISBN 10 : 0387945792
ISBN 13 : 9780387945798
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Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
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Discrete-Time Markov Control Processes: Basic Optimality Criteria (Applications of Mathematics, Volume 30)
Edité par
Springer, 1996
ISBN 10 : 0387945792
ISBN 13 : 9780387945798
Ancien ou d'occasion
Couverture rigide
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Hardcover. Etat : Like New. Like New. book. N° de réf. du vendeur ERICA77303879457926
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