Learning Bayesian Networks with Local Structure
This work addresses the challenge of improving Bayesian network learning for data analysis, though it appears incremental as it builds on existing methods by adding local structure representation.
The paper tackles the problem of learning Bayesian networks from data by introducing a method that learns local structure in conditional probability tables, enabling models with variable parameters that better capture data interactions. The evaluation shows that learning curves converge faster and the resulting networks are more complex in arcs but require fewer parameters.
In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly represents and learns the local structure in the conditional probability tables (CPTs), that quantify these networks. This increases the space of possible models, enabling the representation of CPTs with a variable number of parameters that depends on the learned local structures. The resulting learning procedure is capable of inducing models that better emulate the real complexity of the interactions present in the data. We describe the theoretical foundations and practical aspects of learning local structures, as well as an empirical evaluation of the proposed method. This evaluation indicates that learning curves characterizing the procedure that exploits the local structure converge faster than these of the standard procedure. Our results also show that networks learned with local structure tend to be more complex (in terms of arcs), yet require less parameters.