Ergodic Behavior of Markov Processes With Applications to Limit Theorems

The general topic of this book is the ergodic behavior of Markov processes. A detailed introduction to methods for proving ergodicity and upper bounds for ergodic rates is presented in the first part of the book, with the focus put on weak ergodic rates, typical for Markov systems with complicated s...

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Bibliographic Details
Main Author: Kulik, Alexei
Format: eBook
Language:English
Published: Berlin ; Boston De Gruyter 2017, ©2018
Series:De Gruyter Studies in Mathematics
Subjects:
Online Access:
Collection: DeGruyter MPG Collection - Collection details see MPG.ReNa
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245 0 0 |a Ergodic Behavior of Markov Processes  |h Elektronische Ressource  |b With Applications to Limit Theorems  |c Alexei Kulik (Institute of MathematicsUkrainian National Academy of Sciences) 
260 |a Berlin ; Boston  |b De Gruyter  |c 2017, ©2018 
300 |a X, 257 Seiten 
505 0 |a Part I: Ergodic Rates for Markov Chains and Processes -- Markov Chains with Discrete State Spaces -- General Markov Chains: Ergodicity in Total Variation -- MarkovProcesseswithContinuousTime -- Weak Ergodic Rates -- Part II: Limit Theorems -- The Law of Large Numbers and the Central Limit Theorem -- Functional Limit Theorems 
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520 |a The general topic of this book is the ergodic behavior of Markov processes. A detailed introduction to methods for proving ergodicity and upper bounds for ergodic rates is presented in the first part of the book, with the focus put on weak ergodic rates, typical for Markov systems with complicated structure. The second part is devoted to the application of these methods to limit theorems for functionals of Markov processes. The book is aimed at a wide audience with a background in probability and measure theory. Some knowledge of stochastic processes and stochastic differential equations helps in a deeper understanding of specific examples. 
520 1 |a "This book is clearly written, with many examples, and will be of interest to advanced students and researchers who wish to learn more about quantitative ergodic theory for Markov processes." Max Fathi in: Mathematical Reviews Clippings (2019) MR3791835