Markov Processes: Characterization and Convergence

by Stewart N. Ethier (University of Utah ); Thomas G. Kurtz (University of Wisconsin-Madison )

• ISBN13: 9780470317327
• ISBN10: 0470317329
• Format: eBook
• Copyright: 2009-09-01
• Publisher: Wiley

Summary

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 fAAr 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.

Table of Contents

Operator Semigroups

Stochastic Processes and Martingales

Convergence of Probability Measures

Generators and Markov Processes

Stochastic Integral Equations

Random Time Changes

Invariance Principles and Diffusion Approximations

Examples of Generators

Branching Processes

Genetics Models

Density Dependent Population Processes

Random Evolutions

Appendixes

References

Index

Table of Contents provided by Publisher. All Rights Reserved.