Probalistic Network Construction Using the Minimum Description Length Principle

Probabilistic networks can be constructed from a database of cases by selecting a network that has highest quality with respect to this database according to a given measure. A new measure is presented for this purpose based on a minimum description length (MDL) approach. This measure is compared with a commonly used measure based on a Bayesian approach both from a theoretical and an experimental point of view. We show that the two measures have the same properties for innnite large databases. For smaller databases, however, the MDL measure assigns equal quality to networks that represent the same set of independencies while the Bayesian measure does not. Preliminary test results suggest that an algorithm for learning probabilistic networks using the minimum description length approach performs comparably to a learning algorithm using the Bayesian approach. However, the former is slightly faster.