2 research outputs found
Learning Bayesian networks with low inference complexity
One of the main research topics in machine
learning nowadays is the improvement of the inference and
learning processes in probabilistic graphical models. Traditionally,
inference and learning have been treated separately,
but given that the structure of the model conditions the
inference complexity, most learning methods will sometimes
produce inefficient inference models. In this paper we
propose a framework for learning low inference complexity
Bayesian networks. For that, we use a representation of
the network factorization that allows efficiently evaluating
an upper bound in the inference complexity of each model
during the learning process. Experimental results show that
the proposed methods obtain tractable models that improve
the accuracy of the predictions provided by approximate
inference in models obtained with a well-known Bayesian
network learner