Belief Update in CLG Bayesian Networks With Lazy Propagation
This work addresses computational efficiency in probabilistic inference for mixed continuous-discrete Bayesian networks, but it appears incremental as an extension of existing methods.
The authors tackled the problem of exact belief update in Conditional Linear Gaussian Bayesian Networks (CLG BNs) by extending lazy propagation with factor decomposition, and the result showed significant potential in performance based on preliminary empirical evaluation.
In recent years Bayesian networks (BNs) with a mixture of continuous and discrete variables have received an increasing level of attention. We present an architecture for exact belief update in Conditional Linear Gaussian BNs (CLG BNs). The architecture is an extension of lazy propagation using operations of Lauritzen & Jensen [6] and Cowell [2]. By decomposing clique and separator potentials into sets of factors, the proposed architecture takes advantage of independence and irrelevance properties induced by the structure of the graph and the evidence. The resulting benefits are illustrated by examples. Results of a preliminary empirical performance evaluation indicate a significant potential of the proposed architecture.