  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.
