<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Chaari, S.</style></author><author><style face="normal" font="default" size="100%">Cipriano, F.</style></author><author><style face="normal" font="default" size="100%">Gheryani, Soumaya</style></author><author><style face="normal" font="default" size="100%">Ouerdiane, H.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sanov's Theorem for White Noise Distributions and Application to the Gibbs Conditioning Principle</style></title><secondary-title><style face="normal" font="default" size="100%">ACTA APPLICANDAE MATHEMATICAE</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Gibbs conditioning principle}</style></keyword><keyword><style  face="normal" font="default" size="100%">Large Deviation Principle</style></keyword><keyword><style  face="normal" font="default" size="100%">Sanov's theorem</style></keyword><keyword><style  face="normal" font="default" size="100%">{Positive White Noise distributions</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">{DEC}</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">{3}</style></number><publisher><style face="normal" font="default" size="100%">{SPRINGER}</style></publisher><pub-location><style face="normal" font="default" size="100%">{VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS}</style></pub-location><volume><style face="normal" font="default" size="100%">104</style></volume><pages><style face="normal" font="default" size="100%">313-324</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;{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 &amp;gt;= 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).}&lt;/p&gt;
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