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Synopsis
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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From the Back Cover
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
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Markov Chains and Invariant Probabilities (Paperback)
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Paperback. Condition: new. Paperback. 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). = {~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, . 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 ~. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9783034894081
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Markov Chains and Invariant Probabilities (Progress in Mathematics)
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Markov Chains and Invariant Probabilities
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Markov Chains and Invariant Probabilities (Progress in Mathematics)
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Markov Chains and Invariant Probabilities (Progress in Mathematics)
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Markov Chains and Invariant Probabilities
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Markov Chains and Invariant Probabilities
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Birkhäuser Basel, Birkhäuser Okt 2012, 2012
ISBN 10: 3034894082
ISBN 13: 9783034894081
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Taschenbuch. Condition: 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). 228 pp. Englisch. Seller Inventory # 9783034894081
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Markov Chains and Invariant Probabilities
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Condition: 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. Seller Inventory # 95264985
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Markov Chains and Invariant Probabilities
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Condition: New. Series: Progress in Mathematics. Num Pages: 208 pages, biography. BIC Classification: KJT; PBT; PHU. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 12. Weight in Grams: 355. . 2012. Softcover reprint of the original 1st ed. 2003. Paperback. . . . . Seller Inventory # V9783034894081
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