Using Mutual Information to determine Relevance in Bayesian Networks

The control of Bayesian network (BN) evaluation is important in the development of real-time decision making systems. Techniques which focus attention by considering the relevance of variables in a BN allow more efficient use of computational resources. The statistical concept of mutual information (MI) between two related random variables can be used to measure relevance. We extend this idea to present a new measure of arc weights in a BN, and show how these can be combined to give a measure of the weight of a region of connected nodes. A heuristic path weight of a node or region relative to a specific query is also given. We present results from experiments which show that the MI weights are better than another measure based on the Bhattacharyya distance

treeNets: A Framework for Anytime Evaluation of Belief Networks

We present a new framework for implementing evaluation of belief networks (BNs), which consists of two steps: (1) transforming a belief network into a tree structure called a treeNet (2) performing anytime inference by searching the treeNet. The root of the treeNet represents the query node. Once the treeNet has been constructed, whenever new evidence is incorporated, the posterior probability of the query node is re-calculated, using a variation of the polytree message-passing algorithm. The treeNet framework is geared towards anytime evaluation. Evaluating the treeNet is a tree search problem and we investigate different tree search strategies. By using a best-first method, we are able to increase the rate of convergence of the anytime result