Discovering Weakly-Interacting Factors in a Complex Stochastic Process

Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approximate inference involves grouping the variables in the process into smaller factors and keeping independent beliefs over these factors. In this paper we present several techniques for decomposing a dynamic Bayesian network automatically to enable factored inference. We examine a number of features of a DBN that capture different types of dependencies that will cause error in factored inference. An empirical comparison shows that the most useful of these is a heuristic that estimates the mutual information introduced between factors by one step of belief propagation. In addition to features computed over entire factors, for efficiency we explored scores computed over pairs of variables. We present search methods that use these features, pairwise and not, to find a factorization, and we compare their results ...

• Type: Conference
• Year: 2007
• Where: NIPS
• Authors: Charlie Frogner, Avi Pfeffer
• Keywords: Dynamic Bayesian Network, Exact Inference, Factored Inference, Information Technology, NIPS 2007