+353-1-416-8900REST OF WORLD
+44-20-3973-8888REST OF WORLD
1-917-300-0470EAST COAST U.S
1-800-526-8630U.S. (TOLL FREE)


Markov Processes. Characterization and Convergence. Edition No. 1. Wiley Series in Probability and Statistics

  • ID: 2178066
  • Book
  • October 2005
  • 552 Pages
  • John Wiley and Sons Ltd
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.
Note: Product cover images may vary from those shown

1. Operator Semigroups.

2. Stochastic Processes and Martingales.

3. Convergence of Probability Measures.

4. Generators and Markov Processes.

5. Stochastic Integral Equations.

6. Random Time Changes.

7. Invariance Principles and Diffusion Approximations.

8. Examples of Generators.

9. Branching Processes.

10. Genetic Models.

11. Density Dependent Population Processes.

12. Random Evolutions.





Note: Product cover images may vary from those shown
Stewart N. Ethier University of Utah.

Thomas G. Kurtz University of Wisconsin-Madison.
Note: Product cover images may vary from those shown