compositional models; marginalization; Bayesian network
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.
 Jensen F. V.: Bayesian Networks and Decision Graphs
. Springer Verlag, New York 2001 MR 1876880
 Jiroušek R.: Marginalization in composed probabilistic models. In: Proc. 16th Conf. Uncertainty in Artificial Intelligence UAI’00 (C. Boutilier and M. Goldszmidt, eds.), Morgan Kaufmann, San Francisco 2000, pp. 301–308
 Jiroušek R.: What is the difference between Bayesian networks and compositional models? In: Proc. 7th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty (H. Noguchi, H. Ishii, M. Inuiguchi, eds.), Awaji Yumebutai ICC 2004, pp. 191–196