The Gibbs principle for Markov jump processes

Adnan Aboulalaâ

Stochastic Processes and their Applications, 1996, vol. 64, issue 2, 257-271

Abstract: This paper is devoted to derive a stochastic process version of the "Gibbs principle". Namely, we calculate the law of a jump process (Xt, t [var epsilon] [0, T] given the condition that the empirical energy function of N copies of the process, remains in some domain for all t [var epsilon] [0, T], when N is large. The main tools are Csiszár's theory on conditional limit theorems and a law of large numbers in non-separable Banach spaces.

Keywords: Kullback-Leibler; Information; Generalized; I-projection; Large; deviations; Maximum; entropy; principle; Markov; jump; processes (search for similar items in EconPapers)
Date: 1996
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