Markov Chains and Invariant Probabilities (Progress in Mathematics)
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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). Hard cover.

Von Händler/Antiquariat, The Book Depository EURO [60485773], London, United Kingdom.
Language: English Brand New Book ***** Print on Demand *****.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).

Von Händler/Antiquariat, BuySomeBooks [52360437], Las Vegas, NV, U.S.A.
Hardcover. 208 pages. Dimensions: 9.1in. x 6.3in. x 0.7in.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 (k1 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). This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN.

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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.

Von Händler/Antiquariat, Herb Tandree Philosophy Books [17426], Stroud, GLOS, United Kingdom.
9783764370008 Hardback, This listing is a new book, a title currently in-print which we order directly and immediately from the publisher.

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Von Händler/Antiquariat, Book Deals [60506629], Lewiston, NY, U.S.A.
Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: Preliminaries .- Markov Chains and Ergodic Theorems .- Countable Markov Chains .- Harris Markov Chains .- Markov Chains in Metric Spaces .- Classification of Markov Chains via Occupation Measures .- Feller Markov Chains .- The Poisson Equation .- Strong and Uniform Ergodicity .- Existence of Invariant Probability Measures .- Existence and Uniqueness of Fixed Points for Markov Operators .- Approximation Procedures for Invariant Probability Measures.

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