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Bína, Vladislav; Jiroušek, Radim

Marginalization in multidimensional compositional models. (English) Zbl 1249.65010

Kybernetika 42, No. 4, 405-422 (2006).
Summary: 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.

Cited in 4 Documents

MSC:

65C50 Other computational problems in probability (MSC2010)
60E99 Distribution theory
68T37 Reasoning under uncertainty in the context of artificial intelligence

Keywords:

compositional model; marginalization; Bayesian network; algorithms; graphical Markov models
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\textit{V. Bína} and \textit{R. Jiroušek}, Kybernetika 42, No. 4, 405--422 (2006; Zbl 1249.65010)
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