Ex-library with stamp and library-signature. GOOD condition, some traces of use. Ancien Exemplaire de biblioth�que avec signature et cachet. BON �tat, quelques traces d'usure. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. 60 HER 9783764370008 Sprache: Englisch Gewicht in Gramm: 550.

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Detalles bibliogr�ficos

T�tulo

Markov chains and invariant probabilities On�simo Hern�ndez-Lerma, Jean Bernard Lasserre. Progress in mathematics 211.

Editor

Basel, Birkh�user Verlag

Fecha de publicaci�n

2003

Idioma

Ingl�s

ISBN 10

3764370009

ISBN 13

9783764370008

Encuadernaci�n

Hardcover

Cat�logos de vendedores

SA MATHEMATIK

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Sinopsis

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).

De la contraportada

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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