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Markov Chains and Invariant Probabilities
Hernandez-Lerma, Onesimo; Lasserre, Jean-Bernard
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Description for Markov Chains and Invariant Probabilities
Paperback. 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.
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
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
Paperback
Publication date
2012
Publisher
Springer Basel Switzerland
Number of pages
208
Condition
New
Series
Progress in Mathematics
Number of Pages
208
Place of Publication
Basel, Switzerland
ISBN
9783034894081
SKU
V9783034894081
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
MathSciNet