Abstract: Probabilistic logical models have proven to be very successful at modelling uncertain, complex relational data. Most current formalisms and implementations focus on modelling domains that only have discrete variables. Yet many real-world problems are hybrid and have both discrete and continuous variables. In this paper we focus on the Logical Bayesian Network (LBN) formalism. This paper discusses our work in progress in developing hybrid LBNs, which offer support for both discrete and continuous variables. We provide a brief sketch for basic parameter learning and inference algorithms for them.
Publication Year: 2012
Publication Date: 2012-01-01
Language: en
Type: article
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