Bayesian network refinement via machine learning approach

An approach to refining Bayesian network structures from new data is developed. Most previous work has only considered the refinement of the network's conditional probability parameters and has not addressed the issue of refining the network's structure. We tackle this problem by a machine learning approach based on a formalism known as the minimum description length (MDL) principle. The MDL principle is well suited to this task since it can perform tradeoffs between the accuracy, simplicity, and closeness to the existent structure. Another salient feature of this refinement approach is the capability of refining a network structure using partially specified data. Moreover, a localization scheme is developed for efficient computation of the description lengths since direct evaluation involves exponential time resources.