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}, month = {{DEC}}, pages = {313-324}, publisher = {{SPRINGER}}, type = {{Article}}, address = {{VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS}}, 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){\textquoteright}(N{\textquoteright})(+) 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).}

}, keywords = {Gibbs conditioning principle}, Large Deviation Principle, Sanov{\textquoteright}s theorem, {Positive White Noise distributions}, issn = {{0167-8019}}, doi = {{10.1007/s10440-008-9259-6}}, author = {Chaari, S. and Cipriano, F. and Gheryani, Soumaya and Ouerdiane, H.} }