@misc{10481/32709,
year = {2006},
url = {http://hdl.handle.net/10481/32709},
abstract = {We propose a new scoring function for learning Bayesian networks from data using score+search algorithms. This is based on the concept of mutual information and exploits some well-known properties of this measure in a novel way. Essentially, a statistical independence test based on the
chi-square distribution, associated with the mutual information measure, together with a property of additive decomposition of this measure, are combined in order to measure the degree of interaction between each variable and its parent variables in the network. The result is a non-Bayesian
scoring function called MIT (mutual information tests) which belongs to the family of scores based on information theory. The MIT score also represents a penalization of the Kullback-Leibler divergence between the joint probability distributions associated with a candidate network and with the available data set. Detailed results of a complete experimental evaluation of the proposed scoring
function and its comparison with the well-known K2, BDeu and BIC/MDL scores are also presented.},
organization = {I would like to acknowledge support for this work from the Spanish ‘Consejería de Innovación
Ciencia y Empresa de la Junta de Andalucía’, under Project TIC-276.},
publisher = {MIT Press},
keywords = {Bayesian networks},
keywords = {Scoring functions},
keywords = {Learning},
keywords = {Mutual information},
keywords = {Conditional independence tests},
title = {A scoring function for learning Bayesian networks based on mutual information and conditional independence tests},
author = {Campos Ibáñez, Luis Miguel},
}