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 shared binary decision diagram (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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© 2011 Springer-Verlag Berlin Heidelberg
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Ishihata, M., Sato, T., Minato, Si. (2011). Compiling Bayesian Networks for Parameter Learning Based on Shared BDDs. In: Wang, D., Reynolds, M. (eds) AI 2011: Advances in Artificial Intelligence. AI 2011. Lecture Notes in Computer Science(), vol 7106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25832-9_21
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DOI: https://doi.org/10.1007/978-3-642-25832-9_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25831-2
Online ISBN: 978-3-642-25832-9
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