Ethier / Kurtz Markov Processes

Characterization and Convergence
1. Auflage 2009
ISBN: 978-0-470-31732-7
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 552 Seiten, E-Book

Reihe: Wiley Series in Probability and Statistics

The Wiley-Interscience Paperback Series consists of selected booksthat have been made more accessible to consumers in an effort toincrease global appeal and general circulation. With these newunabridged softcover volumes, Wiley hopes to extend the lives ofthese works by making them available to future generations ofstatisticians, mathematicians, and scientists.
"[A]nyone who works with Markov processes whose state space isuncountably infinite will need this most impressive book as a guideand reference."
-American Scientist
"There is no question but that space should immediately be reservedfor [this] book on the library shelf. Those who aspire to masteryof the contents should also reserve a large number of long winterevenings."
-Zentralblatt für Mathematik und ihre Grenzgebiete/MathematicsAbstracts
"Ethier and Kurtz have produced an excellent treatment of themodern theory of Markov processes that [is] useful both as areference work and as a graduate textbook."
-Journal of Statistical Physics
Markov Processes presents several different approaches to provingweak approximation theorems for Markov processes, emphasizing theinterplay of methods of characterization and approximation.Martingale problems for general Markov processes are systematicallydeveloped for the first time in book form. Useful to theprofessional as a reference and suitable for the graduate studentas a text, this volume features a table of the interdependenciesamong the theorems, an extensive bibliography, and end-of-chapterproblems.

Autoren/Hrsg.

Ethier, Stewart N.

Kurtz, Thomas G.

Inhaltsverzeichnis

Introduction.
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.
Appendixes.
References.
Index.
Flowchart.

Über Autor(innen)

STEWART N. ETHIER, PhD, is Professor of Mathematics at theUniversity of Utah. He received his PhD in mathematics at theUniversity of Wisconsin-Madison.
THOMAS G. KURTZ, PhD, is Professor of Mathematics and Statisticsat the University of Wisconsin-Madison. He is a Book ReviewEditor for The Annals of Probability and the author ofApproximation of Population Processes. Dr. Kurtz obtained his PhDin mathematics at Stanford University.