On maximization of the information divergence from an exponential family

František Matúš and Nihat Ay

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Submission date: 17. May. 2003
Pages: 7
published in: Proceedings of 6th workshop on uncertainty processing : Hejnice, September 24-27, 2003
[Praha] : Oeconomica, 2003. - P. 199 - 204 
Bibtex
MSC-Numbers: 94A17, 62B10, 60A10
Keywords and phrases: kullback-leibler divergence, information projection, exponential family, infomax principle
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Abstract:
The information divergence of a probability measure P from an exponential family over a finite set is defined as infimum of the divergences of P from Q subject to Q in . For convex exponential families the local maximizers of this function of P are found. General exponential family of dimension d is enlarged to an exponential family of the dimension at most 3d+2 such that the local maximizers are of zero divergence from .