Large deviations for stochastic processes Feng, Jin Kurtz, Thomas G. Eurospan 9780821841457 : Focuses on the results on large deviations for a class of stochastic processes. Following an introduction and overview, this book gives necessary conditions for exponential tightness that are analogous to conditions for tightness in the theory of weak convergence and focuses on Markov processes in metric spaces.

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

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.

Mathematical Surveys and Monographs Volume: 131; 2006; 410 pp; Softcover MSC . Jin Feng; Thomas G. Kurtz. The book is devoted to the results on large deviations for a class of stochastic processes.

Mathematical Surveys and Monographs Volume: 131; 2006; 410 pp; Softcover MSC: Primary 60; 47; Secondary 49. Print ISBN: 978-1-4704-1870-0 Product Code: SURV/131.

oceedings{Feng2006LargeDF, title {Large Deviations for Stochastic Processes}, author {Jin Feng and T. G. . Kurtz}, year {2006} }. Jin Feng, T. The notes are devoted to results on large deviations for sequences of Markov processes following closely the book by Feng and Kurtz (). We outline how convergence of Fleming's nonlinear semigroups (logarithmically transformed nonlinear semigroups) implies large deviation principles analogous to the use of convergence of linear semigroups in weak convergence.

Monographs Volume 131. Large Deviations for Stochastic Processes. Jin Feng Thomas G. American Mathematical Society. The book is devoted to the results on large deviations for a class of stochastic processes

Monographs Volume 131. Part 1 gives necessary and suf-cient conditions for exponential tightness that are analogous to conditions for tightness in the theory of weak convergence.

JOURNAL NAME: Applied Mathematics, Vo. N. 2A, December 31, 2012. ABSTRACT: In this paper we examine the large deviations principle (LDP) for sequences of classic Cramér-Lundberg risk processes under suitable time and scale modifications, and also for a wide class of claim distributions including (the non-super- exponential) exponential claims.

University of Wisconsin–Madison. From a mathematical perspective, the rigorous analysis of the tails of a distribution is known as large deviation theory, which provides a rigorous proba- bilistic framework for interpreting the WKB solution in terms of optimal fluctuational paths. The analysis of metastability in chemical master equations has been devel- oped along analogous lines to SDEs, combining WKB methods and large deviation principles with path-integral or operator methods.

Start by marking Large Deviations for Stochastic Processes as Want to Read . This work is devoted to the results on large deviations for a class of stochastic processes.

Start by marking Large Deviations for Stochastic Processes as Want to Read: Want to Read savin. ant to Read.

Large Deviations for Stochastic Processes (American Mathematical Society 2006): This book with his former P. Jin Feng, presents a general theory for obtaining large deviation results for a large class of stochastic processes. This theory is based on the idea that the large deviation principle for a sequence of Markov processes can be obtained by proving the convergence of an associated family of nonlinear semigroups