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Sinopsis
This book concerns discrete-time homogeneous Markov chains that admit an invariant probability measure. The main objective is to give a systematic, self-contained presentation on some key issues about the ergodic behavior of that class of Markov chains. These issues include, in particular, the various types of convergence of expected and pathwise occupation measures, and ergodic decompositions of the state space.
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This book concerns discrete-time homogeneous Markov chains that admit an invariant probability measure. The main objective is to give a systematic, self-contained presentation on some key issues about the ergodic behavior of that class of Markov chains. These issues include, in particular, the various types of convergence of expected and pathwise occupation measures, and ergodic decompositions of the state space. Some of the results presented appear for the first time in book form. A distinguishing feature of the book is the emphasis on the role of expected occupation measures to study the long-run behavior of Markov chains on uncountable spaces.
The intended audience are graduate students and researchers in theoretical and applied probability, operations research, engineering and economics.
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Markov Chains and Invariant Probabilities (Progress in Mathematics)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Markov Chains and Invariant Probabilities
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is about discrete-time, time-homogeneous, Markov chains (Mes) and their ergodic behavior. To this end, most of the material is in fact about stable Mes, by which we mean Mes that admit an invariant probability measure. To state this more precisely and give an overview of the questions we shall be dealing with, we will first introduce some notation and terminology. Let (X,B) be a measurable space, and consider a X-valued Markov chain ~. = {~k' k = 0, 1, . } with transition probability function (t.pJ.) P(x, B), i.e., P(x, B) := Prob (~k+1 E B I ~k = x) for each x E X, B E B, and k = 0,1, . The Me ~. is said to be stable if there exists a probability measure (p.m.) /.l on B such that (\*) VB EB. /.l(B) = Ix /.l(dx) P(x, B) If (\*) holds then /.l is called an invariant p.m. for the Me ~. (or the t.p.f. P). Nº de ref. del artículo: 9783034894081
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Markov Chains and Invariant Probabilities
Impresión bajo demandaLibrería: moluna, Greven, Alemania
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Some of the results presented appear for the first time in book formEmphasis on the role of expected occupation measures to study the long-run behavior of Markov chains on uncountable spacesThis book is about discrete-time, time-homoge. Nº de ref. del artículo: 4319225
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Markov Chains and Invariant Probabilities (Progress in Mathematics)
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Paperback. Condición: New. Nº de ref. del artículo: 6666-IUK-9783034894081
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Markov Chains and Invariant Probabilities
Librería: Books Puddle, New York, NY, Estados Unidos de America
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Condición: New. pp. 228. Nº de ref. del artículo: 2698180870
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Markov Chains and Invariant Probabilities
Impresión bajo demandaLibrería: Majestic Books, Hounslow, Reino Unido
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Condición: New. Print on Demand pp. 228 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam. Nº de ref. del artículo: 95264985
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Markov Chains and Invariant Probabilities
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Birkhäuser Basel, Birkhäuser Basel Okt 2012, 2012
ISBN 10: 3034894082
ISBN 13: 9783034894081
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is about discrete-time, time-homogeneous, Markov chains (Mes) and their ergodic behavior. To this end, most of the material is in fact about stable Mes, by which we mean Mes that admit an invariant probability measure. To state this more precisely and give an overview of the questions we shall be dealing with, we will first introduce some notation and terminology. Let (X,B) be a measurable space, and consider a X-valued Markov chain ~. = {~k' k = 0, 1, . } with transition probability function (t.pJ.) P(x, B), i.e., P(x, B) := Prob (~k+1 E B I ~k = x) for each x E X, B E B, and k = 0,1, . The Me ~. is said to be stable if there exists a probability measure (p.m.) /.l on B such that (\*) VB EB. /.l(B) = Ix /.l(dx) P(x, B) If (\*) holds then /.l is called an invariant p.m. for the Me ~. (or the t.p.f. P).Springer Basel AG in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 228 pp. Englisch. Nº de ref. del artículo: 9783034894081
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Markov Chains and Invariant Probabilities (Progress in Mathematics)
Librería: Revaluation Books, Exeter, Reino Unido
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Paperback. Condición: Brand New. 228 pages. 9.25x6.10x0.52 inches. In Stock. Nº de ref. del artículo: x-3034894082
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Markov Chains and Invariant Probabilities
Impresión bajo demandaLibrería: Biblios, Frankfurt am main, HESSE, Alemania
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Condición: New. PRINT ON DEMAND pp. 228. Nº de ref. del artículo: 1898180876
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Markov Chains and Invariant Probabilities
Publicado por
Springer, Basel, Birkhäuser Basel, Birkhäuser Okt 2012, 2012
ISBN 10: 3034894082
ISBN 13: 9783034894081
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is about discrete-time, time-homogeneous, Markov chains (Mes) and their ergodic behavior. To this end, most of the material is in fact about stable Mes, by which we mean Mes that admit an invariant probability measure. To state this more precisely and give an overview of the questions we shall be dealing with, we will first introduce some notation and terminology. Let (X,B) be a measurable space, and consider a X-valued Markov chain ~. = {~k' k = 0, 1, . } with transition probability function (t.pJ.) P(x, B), i.e., P(x, B) := Prob (~k+1 E B I ~k = x) for each x E X, B E B, and k = 0,1, . The Me ~. is said to be stable if there exists a probability measure (p.m.) /.l on B such that (\*) VB EB. /.l(B) = Ix /.l(dx) P(x, B) If (\*) holds then /.l is called an invariant p.m. for the Me ~. (or the t.p.f. P). 208 pp. Englisch. Nº de ref. del artículo: 9783034894081
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