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Marginalization in multidimensional compositional models
Kybernetika, Tome 42 (2006) no. 4, pp. 405-422 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

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Efficient computational algorithms are what made graphical Markov models so popular and successful. Similar algorithms can also be developed for computation with compositional models, which form an alternative to graphical Markov models. In this paper we present a theoretical basis as well as a scheme of an algorithm enabling computation of marginals for multidimensional distributions represented in the form of compositional models.
Efficient computational algorithms are what made graphical Markov models so popular and successful. Similar algorithms can also be developed for computation with compositional models, which form an alternative to graphical Markov models. In this paper we present a theoretical basis as well as a scheme of an algorithm enabling computation of marginals for multidimensional distributions represented in the form of compositional models.
Classification : 60E99, 65C50, 68T37
Keywords: compositional models; marginalization; Bayesian network 
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Bína, Vladislav; Jiroušek, Radim. Marginalization in multidimensional compositional models. Kybernetika, Tome 42 (2006) no. 4, pp. 405-422. http://geodesic.mathdoc.fr/item/KYB_2006_42_4_a1/

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