Using Causal Information and Local Measures to Learn Bayesian Networks

TL;DR

This paper introduces an improved method for learning Bayesian Networks from data using the MDL principle, incorporating local measures and domain knowledge to enhance search efficiency and network refinement.

Contribution

It presents a new local description length computation, enabling better search algorithms and the integration of partial domain knowledge in Bayesian Network learning.

Findings

Significant improvements in search efficiency

Effective incorporation of domain expert information

Feasibility demonstrated on networks of practical size

Abstract

In previous work we developed a method of learning Bayesian Network models from raw data. This method relies on the well known minimal description length (MDL) principle. The MDL principle is particularly well suited to this task as it allows us to tradeoff, in a principled way, the accuracy of the learned network against its practical usefulness. In this paper we present some new results that have arisen from our work. In particular, we present a new local way of computing the description length. This allows us to make significant improvements in our search algorithm. In addition, we modify our algorithm so that it can take into account partial domain information that might be provided by a domain expert. The local computation of description length also opens the door for local refinement of an existent network. The feasibility of our approach is demonstrated by experiments involving networks of a practical size.