Abstract
Mixtures of distributions concern modeling a probability distribution by a weighted sum of other distributions. Kikuchi approximations of probability distributions follow an approach to approximate the free energy of statistical systems. In this paper, we introduce the mixture of Kikuchi approximations as a probability model. We present an algorithm for learning Kikuchi approximations from data based on the expectation-maximization (EM) paradigm. The proposal is tested in the approximation of probability distributions that arise in evolutionary computation. © Springer-Verlag Berlin Heidelberg 2006.
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Santana, R., Larrañaga, P., & Lozano, J. A. (2006). Mixtures of Kikuchi approximations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4212 LNAI, pp. 365–376). Springer Verlag. https://doi.org/10.1007/11871842_36
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