Exploiting the Probability of Observation for Efficient Bayesian Network Inference

Abstract

It is well-known that the observation of a variable in a Bay-esian network can affect the effective connectivity of the network, which in turn affects the efficiency of inference. Unfortunately, the observed variables may not be known until runtime, which limits the amount of compile-time optimization that can be done in this regard. In this paper, we consider how to improve inference when we know the likelihood of a variable being observed. We show how these probabilities of observation can be exploited to improve existing heuristics for choosing elimination orderings for inference. Empirical tests over a set of benchmark networks using the Variable Elimination algorithm show reductions of up to 50%, 70%, and 55% in multiplications, summations, and runtime, respectively.