Paper Info
Title
Factorization of ZDDs for Representing Bayesian Networks Based on d-Separations.
Abstract
Multi-Linear Functions MLFs is a well known way of probability calculation based on Bayesian Networks BNs. For a given BN, we can calculate the probability in a linear time to the size of MLF. However, the size of MLF grows exponentially with the size of BN, so the computation requires exponential time and space. Minato et al. have shown an efficient method of calculating the probability by using Zero-Suppressed BDDs ZDDs. This method is more effective than the conventional approach of Darwiche et al. which encodes BNs into Conjunctive Normal Forms CNFs and then translates CNFs into factored MLFs. In this article, we present an improvement of Minato's method by factoring ZDDs of MLFs into more factored form utilizing weak divison operation based on d-separation structure of BNs.
Year
DOI
Venue
2015
10.1007/978-3-319-28379-1_12
AMBN@JSAI-isAI
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
5
2
Name
Order
Citations
PageRank
Shan Gao155.17
Shin-ichi Minato200.68