Ergodic behavior of Markov processes : with applications to limit theorems / Alexei Kulik.

By:

Kulik, Alexei [author.]

Material type: Text

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783110458947
3110458942
9783110458718
3110458713
9783110458930
3110458934

Subject(s):

Genre/Form:

Additional physical formats: Print version:: Ergodic behavior of Markov processes.; No title; No titleDDC classification:

519.233 23

LOC classification:

QA274.7 .K85 2018eb

Online resources:

Click here to access online

Contents:

Markov chains with discrete state spaces -- General Markov chains: ergodicity in total variation -- Markov processes with continuous time -- Weak ergodic rates -- The law of large numbers and the central limit theorem -- Functional limit theorems.

Summary: 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.-- Provided by publisher.

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Electronic-Books	OPJGU Sonepat- Campus	E-Books EBSCO			Available

Includes bibliographical references (pages 249-254) and index.

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.-- Provided by publisher.

Online resource; title from PDF title page (EBSCO, viewed February 6, 2018)

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