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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| Format: | Book |
| Language: | English |
| Published: |
Berlin ; Boston :
De Gruyter,
[2017]
Berlin [Germany] : 2018 Berlin, [Germany] ; Boston : [2018] Berlin, [Germany] ; Boston, [Massachusetts] : 2018 |
| Series: | De Gruyter studies in mathematics ;
67 De Gruyter studies in mathematics ; Volume 67 De Gruyter studies in mathematics 67 |
| Subjects: |
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| 245 | 1 | 0 | |a Ergodic behavior of Markov processes : |b with applications to limit theorems / |c Alexei Kulik |
| 264 | 1 | |a Berlin ; |a Boston : |b De Gruyter, |c [2017] | |
| 264 | 1 | |a Berlin [Germany] : |b De Gruyter, |c 2018 | |
| 264 | 1 | |a Berlin, [Germany] ; |a Boston : |b De Gruyter, |c [2018] | |
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| 264 | 4 | |c ©2018 | |
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| 490 | 0 | |a De Gruyter Studies in Mathematics , |x 0179-0986 ; |v 67 | |
| 490 | 1 | |a De Gruyter Studies in Mathematics, |x 0179-0986 ; |v volume 67 | |
| 490 | 1 | |a De Gruyter Studies in Mathematics, |x 01790986 ; |v Volume 67 | |
| 490 | 1 | |a De Gruyter Studies in Mathematics, |x 01790986 ; |v volume 67 | |
| 504 | |a Includes bibliographical references (page 249-254) and author index | ||
| 504 | |a Includes bibliographical references (pages 249-254) and author index | ||
| 504 | |a Includes bibliographical references and index | ||
| 505 | 0 | |a Introduction -- Part I: Ergodic Rates for Markov Chains and Processes -- 1. Markov Chains with Discrete State Spaces -- 2. General Markov Chains: Ergodicity in Total Variation -- 3. Markov Processes with Continuous Time -- 4. Weak Ergodic Rates -- Part II: Limit Theorems -- 5. The Law of Large Numbers and the Central Limit Theorem Show Level 6 Functional Limit Theorems | |
| 505 | 0 | 0 | |t Frontmatter -- |t Preface -- |t Contents -- |t Introduction -- |t Part I: Ergodic Rates for Markov Chains and Processes -- |t 1. Markov Chains with Discrete State Spaces -- |t 2. General Markov Chains: Ergodicity in Total Variation -- |t 3. Markov Processes with Continuous Time -- |t 4. WeakErgodicRates -- |t Part II: Limit Theorems -- |t 5. The Law of Large Numbers and the Central Limit Theorem -- |t 6. Functional Limit Theorems -- |t Bibliography -- |t Index |
| 506 | |a Access restricted by licensing agreement | ||
| 506 | |a Restricted for use by site license. | ||
| 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. Contents Part I: Ergodic Rates for Markov Chains and ProcessesMarkov Chains with Discrete State SpacesGeneral Markov Chains: Ergodicity in Total VariationMarkovProcesseswithContinuousTimeWeak Ergodic Rates Part II: Limit TheoremsThe Law of Large Numbers and the Central Limit TheoremFunctional Limit Theorems | ||
| 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.--Provided by publisher | ||
| 538 | |a Mode of access: Internet via World Wide Web | ||
| 546 | |a In English | ||
| 588 | |a Description based on online resource; title from PDF title page (EBC, viewed December 22, 2017) | ||
| 588 | 0 | |a Description based on online resource; title from PDF title page (publisher's Web site, viewed 31. Jan 2022) | |
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed February 6, 2018) | |
| 590 | |a Access is available to the Yale community | ||
| 596 | |a 22 | ||
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| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General |2 bisacsh | |
| 650 | 7 | |a Markov processes |2 fast | |
| 653 | |a Markov processes | ||
| 653 | |a ergodic rates | ||
| 653 | |a ergodicity | ||
| 653 | |a limit theorems | ||
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