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Computing marginals from the marginal representation in Markov trees

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Advances in Intelligent Computing — IPMU '94 (IPMU 1994)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 945))

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Abstract

Local computational techniques have been proposed to compute marginals for the variables in belief networks or valuation networks, based on the secondary structures called clique trees or Markov trees. However, these techniques only compute the marginal on the subset of variables contained in one node of the secondary structure. This paper presents a method for computing the marginal on the subset that may not be contained in one node. The proposed method allows us to change the structure of the Markov tree without changing any information contained in the nodes, thus avoids the possible repeated computations. Moreover, it can compute the marginal on any subset from the marginal representation already obtained. An efficient implementation of this method is also proposed.

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Authors and Affiliations

  1. IRIDIA and Service d'Automatique, Université libre de Bruxelles, 50, Ave. F. Roosevelt, CP194/6, 1050, Bruxelles, Belgique

    Hong Xu

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  1. Hong Xu
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Editor information

Bernadette Bouchon-Meunier Ronald R. Yager Lotfi A. Zadeh

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© 1995 Springer-Verlag Berlin Heidelberg

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Xu, H. (1995). Computing marginals from the marginal representation in Markov trees. In: Bouchon-Meunier, B., Yager, R.R., Zadeh, L.A. (eds) Advances in Intelligent Computing — IPMU '94. IPMU 1994. Lecture Notes in Computer Science, vol 945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0035942

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  • DOI: https://doi.org/10.1007/BFb0035942

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60116-6

  • Online ISBN: 978-3-540-49443-0

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