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Hernandez-Lerma, Onesimo; Lasserre, Jean-Bernard - Markov Chains and Invariant Probabilities - 9783764370008 - V9783764370008
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Markov Chains and Invariant Probabilities

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Description for Markov Chains and Invariant Probabilities Hardback. Concerning discrete-time homogeneous Markov chains that admit an invariant probability measure, this book aims to give a presentation on some key issues about the ergodic behavior of these chains. These issues include the various types of convergence of expected and pathwise occupation measures, and ergodic decompositions of the state space. Series: Progress in Mathematics. Num Pages: 208 pages, biography. BIC Classification: PBWL. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 234 x 156 x 14. Weight in Grams: 486.
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 ... Read more

Product Details

Format
Hardback
Publication date
2003
Publisher
Birkhauser Verlag AG Switzerland
Number of pages
208
Condition
New
Series
Progress in Mathematics
Number of Pages
208
Place of Publication
Basel, Switzerland
ISBN
9783764370008
SKU
V9783764370008
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15

Reviews for Markov Chains and Invariant Probabilities
"It should be stressed that an important part of the results presented is due to the authors. . . . In the reviewer's opinion, this is an elegant and most welcome addition to the rich literature of Markov processes."
MathSciNet

Goodreads reviews for Markov Chains and Invariant Probabilities


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