I generalise the arguments of [Chow & Liu 1968] to show that a Bayesian network satisfying some arbitrary constraint that best approximates a probability distribution is one for which mutual information weight is maximised. I give a practical procedure for nding an approximation network and evaluate its application on a range of data sets. Articial intelligence requires the ability to reach conclusions that may be far from certain. For example an expert system for medical diagnosis may be given the symptoms of some patient and asked to provide a diagnosis | even though the background knowledge and symptom information may not be enough to determine for sure which problem actually besets the patient. Probability theory provides a plausible model for reasoning under uncertainty, since one would expect a diagnosis to be relatively probable, given the symptoms. This paper addresses practical issues to do with the implementation of probabilistic reasoning. The plan is rst to discuss...

I posit here a dynamic interaction between machine learning and the philosophy of science, and illustrate this claim with the use of a case study involving the foundations of Bayesian networks.

By identifying and pursuing analogies between causal and logical influence I show how the Bayesian network formalism can be applied

presented at

Dependency theory Process theory

Abstract not found