Using Causal Information and Local Measures to Learn Bayesian Networks
This work addresses the challenge of efficiently learning Bayesian Networks for data analysis, but it is incremental as it builds on existing MDL-based methods.
The authors tackled the problem of learning Bayesian Network models from raw data by introducing a new local method for computing description length, which improved the search algorithm and allowed for local refinement and incorporation of domain expert information, with experiments demonstrating feasibility on networks of practical size.
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.