Markov Processes :Characterization and Convergence ( Wiley Series in Probability and Statistics )

Publication subTitle :Characterization and Convergence

Publication series :Wiley Series in Probability and Statistics

Author: Stewart N. Ethier  

Publisher: John Wiley & Sons Inc‎

Publication year: 2009

E-ISBN: 9780470317327

P-ISBN(Paperback): 9780471769866

P-ISBN(Hardback):  9780471081869

Subject: O211.62 Markov process

Language: ENG

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Description

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

"[A]nyone who works with Markov processes whose state space is uncountably infinite will need this most impressive book as a guide and reference."
-American Scientist

"There is no question but that space should immediately be reserved for [this] book on the library shelf. Those who aspire to mastery of the contents should also reserve a large number of long winter evenings."
-Zentralblatt für Mathematik und ihre Grenzgebiete/Mathematics Abstracts

"Ethier and Kurtz have produced an excellent treatment of the modern theory of Markov processes that [is] useful both as a reference work and as a graduate textbook."
-Journal of Statistical Physics

Markov Processes presents several different approaches to proving weak approximation theorems for Markov processes, emphasizing the interplay of methods of characterization and approximation. Martingale problems for general Markov processes are systematically developed for the first time in book form. Useful to the professional as a reference and suitable for the graduate student as a text, this volume features a table of the interdependencies among the theorems, an extensive bibliography, and end-of-chapter problems.

Chapter

CONTENTS

pp.:  5 – 9

Introduction

pp.:  9 – 13

1. Operator Semigroups

pp.:  13 – 18

3. Convergence of Probability Measures

pp.:  61 – 107

4. Generators and Markov Processes

pp.:  107 – 167

5. Stochastic Integral Equations

pp.:  167 – 287

6. Random Time Changes

pp.:  287 – 318

7. Invariance Principles and Diffusion Approximations

pp.:  318 – 349

8. Examples of Generators

pp.:  349 – 377

9. Branching Processes

pp.:  377 – 398

10. Genetic Models

pp.:  398 – 422

11. Density Dependent Population Processes

pp.:  422 – 464

12. Random Evolutions

pp.:  464 – 480

Appendixes

pp.:  480 – 504

References

pp.:  504 – 520

Index

pp.:  520 – 533

Flowchart

pp.:  533 – 541

LastPages

pp.:  541 – 555

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