Learning Hybrid Bayesian Networks from Data Export

@misc{citeulike:93480, abstract = {We illustrate two different methodologies for learning Hybrid Bayesian networks, that is, Bayesian networks containing both continuous and discrete variables, from data. The two methodologies differ in the way of handling continuous data when learning the Bayesian network structure. The first methodology uses discretized data to learn the Bayesian network structure, and the original non-discretized data for the parameterization of the learned structure. The second methodology uses...}, author = {Monti, S. and Cooper, G.}, citeulike-article-id = {93480}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.50.9744}, keywords = {bayesian, bayesnet, continuous, discrete, learning, structure}, posted-at = {2005-12-29 17:30:21}, priority = {2}, title = {Learning Hybrid Bayesian Networks from Data}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.50.9744} }

TY - GEN ID - citeulike:93480 L3 - citeulike-article-id:93480 N2 - We illustrate two different methodologies for learning Hybrid Bayesian networks, that is, Bayesian networks containing both continuous and discrete variables, from data. The two methodologies differ in the way of handling continuous data when learning the Bayesian network structure. The first methodology uses discretized data to learn the Bayesian network structure, and the original non-discretized data for the parameterization of the learned structure. The second methodology uses... TI - Learning Hybrid Bayesian Networks from Data KW - bayesian KW - bayesnet KW - continuous KW - discrete KW - learning KW - structure AU - Monti, S AU - Cooper, G UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.50.9744 ER -