mfsm

Citation:

Chaari, S., F. Cipriano, Soumaya Gheryani, and H. Ouerdiane. "Sanov's Theorem for White Noise Distributions and Application to the Gibbs Conditioning Principle." ACTA APPLICANDAE MATHEMATICAE. 104 (2008): 313-324.

Abstract:

{We consider a positive distribution Phi such that Phi defines a probability measure mu = mu Phi on the dual of some real nuclear Frechet space. A large deviation principle is proved for the family \{mu(n), n >= 1\} where mu(n) denotes the image measure of the product measure mu(n)(Phi) under the empirical distribution function L(n). Here the rate function I is defined on the space F(theta)'(N')(+) and agrees with the relative entropy function (H) over tilde (Psi/Phi). As an application, we cite the Gibbs conditioning principle which describes the limiting behaviour as n tends to infinity of the law of k tagged particles Y(1),...,Y(k) under the constraint that L(n)(Y) belongs to some subset A(0).}

Notes:

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