Limit Theorems on Large Deviations for Markov Stochastic Processes

This book PDF is perfect for those who love Mathematics genre, written by A.D. Wentzell and published by Springer Science & Business Media which was released on 06 December 2012 with total hardcover pages 192. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Limit Theorems on Large Deviations for Markov Stochastic Processes books below.

Limit Theorems on Large Deviations for Markov Stochastic Processes
Author : A.D. Wentzell
File Size : 42,5 Mb
Publisher : Springer Science & Business Media
Language : English
Release Date : 06 December 2012
ISBN : 9789400918528
Pages : 192 pages
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Limit Theorems on Large Deviations for Markov Stochastic Processes by A.D. Wentzell Book PDF Summary

In recent decades a new branch of probability theory has been developing intensively, namely, limit theorems for stochastic processes. As compared to classical limit theorems for sums of independent random variables, the generalizations are going here in two directions simultaneously. First, instead of sums of independent variables one considers stochastic processes belonging to certain broad classes. Secondly, instead of the distribution of a single sum - the distribution of the value of a stochastic process at one (time) point - or the joint distribution of the values of a process at a finite number of points, one considers distributions in an infinite-dimensional function space. For stochastic processes constructed, starting from sums of independent random variables, this is the same as considering the joint distribution of an unboundedly increasing number of sums.

Limit Theorems on Large Deviations for Markov Stochastic Processes

In recent decades a new branch of probability theory has been developing intensively, namely, limit theorems for stochastic processes. As compared to classical limit theorems for sums of independent random variables, the generalizations are going here in two directions simultaneously. First, instead of sums of independent variables one considers stochastic

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