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Large Deviations for Stochastic Processes (Mathematical Surveys and Monographs)

Applied Mathematics

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Description

About the Author Jin Feng, University of Kansas, Lawrence, KS.Thomas G. Kurtz, University of Wisconsin at Madison, Madison, WI. Product 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.

Product Specifications

Format: paperback
ASIN: 1470418703
Category: Books > Subjects > Science, Nature & Maths > Mathematics > Applied Mathematics
Domain: Amazon UK
Release Date: 30 December 2006
Listed Since: 16 November 2016

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Large Deviations for Stochastic Processes (Mathematical Surveys and Monographs)

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