Large Deviations for Stochastic Processes (Mathematical Surveys and Monographs, 131)
ISBN-13:
9781470418700
ISBN-10:
1470418703
Author:
Thomas G. Kurtz, Jin Feng
Publication date:
2015
Publisher:
American Mathematical Society
Format:
Paperback
401 pages
FREE US shipping
Book details
ISBN-13:
9781470418700
ISBN-10:
1470418703
Author:
Thomas G. Kurtz, Jin Feng
Publication date:
2015
Publisher:
American Mathematical Society
Format:
Paperback
401 pages
Summary
Large Deviations for Stochastic Processes (Mathematical Surveys and Monographs, 131) (ISBN-13: 9781470418700 and ISBN-10: 1470418703), written by authors
Thomas G. Kurtz, Jin Feng, was published by American Mathematical Society in 2015.
With an overall rating of 3.7 stars, it's a notable title among other
books. You can easily purchase or rent Large Deviations for Stochastic Processes (Mathematical Surveys and Monographs, 131) (Paperback) from BooksRun,
along with many other new and used
books
and textbooks.
And, if you're looking to sell your copy, our current buyback offer is $0.34.
Description
The book is devoted to the results on large deviations for a class of stochastic processes. Following an introduction and overview, the material is presented in three parts. Part 1 gives necessary and sufficient conditions for exponential tightness that are analogous to conditions for tightness in the theory of weak convergence. Part 2 focuses on Markov processes in metric spaces. For a sequence of such processes, convergence of Fleming's logarithmically transformed nonlinear semigroups is shown to imply the large deviation principle in a manner analogous to the use of convergence of linear semigroups in weak convergence. Viscosity solution methods provide applicable conditions for the necessary convergence. Part 3 discusses methods for verifying the comparison principle for viscosity solutions and applies the general theory to obtain a variety of new and known results on large deviations for Markov processes. In examples concerning infinite dimensional state spaces, new comparison principles are derived for a class of Hamilton-Jacobi equations in Hilbert spaces and in spaces of probability measures.
We would LOVE it if you could help us and other readers by reviewing the book
Book review
Congratulations! We have received your book review.
{user}
{createdAt}
by {truncated_author}