Compiling Bayesian Networks for Parameter Learning Based on Shared BDDs

  • Masakazu Ishihata
  • Taisuke Sato
  • Shin-ichi Minato
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7106)

Abstract

Compiling Bayesian networks (BNs) is one of the most effective ways to exact inference because a logical approach enables the exploitation of local structures in BNs (i.e., determinism and context-specific independence). In this paper, a new parameter learning method based on compiling BNs is proposed. Firstly, a target BN with multiple evidence sets are compiled into a single sharedbinarydecisiondiagram (SBDD) which shares common sub-graphs in multiple BDDs. Secondly, all conditional expectations which are required for executing the EM algorithm are simultaneously computed on the SBDD while their common local probabilities and expectations are shared. Due to these two types of sharing, the computation efficiency of the proposed method is higher than that of an EMalgorithm which naively uses an existing BN compiler for exact inference.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Masakazu Ishihata
    • 1
  • Taisuke Sato
    • 1
  • Shin-ichi Minato
    • 2
  1. 1.Tokyo Institute of TechnologyTokyoJapan
  2. 2.Hokkaido UniversitySapporoJapan

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