The Gibbs principle for Markov jump processes

被引：2
|
作者：
Aboulalaa, A
机构：
[1] Laboratoire de Probabilités, Universite Pierre et Marie Curie, 75252 Paris, Tour 56, 3eme etage, 4, Pl. Jussieu
来源：
STOCHASTIC PROCESSES AND THEIR APPLICATIONS | 1996年 / 64卷 / 02期
关键词：
Kullback-Leibler Information; generalized I-projection; large deviations; maximum entropy principle; Markov jump processes;
D O I：
10.1016/S0304-4149(96)00092-0
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
This paper is devoted to derive a stochastic process version of the ''Gibbs principle''. Namely, we calculate the law of a jump process (X(t), t is an element of [0, T]) given the condition that the empirical energy function of N copies of the process, remains in some domain for all t is an element of [0, T], when N is large. The main tools are Csiszar's theory on conditional limit theorems and a law of large numbers in non-separable Banach spaces.
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页码：257 / 271
页数：15
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