Exact Inference in Networks with Discrete Children of Continuous Parents

Many real life domains contain a mixture of discrete and continuous variables and can be modeled as hybrid Bayesian Networks (BNs). An important subclass of hybrid BNs are conditional linear Gaussian (CLG) networks, where the conditional distribution of the continuous variables given an assignment to the discrete variables is a multivariate Gaussian. Lauritzen's extension to the clique tree algorithm can be used for exact inference in CLG networks. However, many domains include discrete variables that depend on continuous ones, and CLG networks do not allow such dependencies to be represented. In this paper, we propose the first "exact" inference algorithm for augmented CLG networks -- CLG networks augmented by allowing discrete children of continuous parents. Our algorithm is based on Lauritzen's algorithm, and is exact in a similar sense: it computes the exact distributions over the discrete nodes, and the exact first and second moments of the continuous ones, up...

• Type: Conference
• Year: 2001
• Where: UAI
• Authors: Uri Lerner, Eran Segal, Daphne Koller
• Keywords: CLG Networks, Continuous Variables, Discrete Variables, UAI 2001, UAI 2008