@article { ISI:000260509700006,
	title = {Sanov{\textquoteright}s Theorem for White Noise Distributions and Application to the Gibbs Conditioning Principle},
	journal = {ACTA APPLICANDAE MATHEMATICAE},
	volume = {104},
	number = {{3}},
	year = {2008},
	note = {<p>n/a</p>
},
	month = {{DEC}},
	pages = {313-324},
	publisher = {{SPRINGER}},
	type = {{Article}},
	address = {{VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS}},
	abstract = {<p>{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 \&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){\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).}</p>
},
	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.}
}
