Bayesian optimization algorithm, decision graphs, and Occam's razor

Abstract

pelikanilligalgeuiucedu This paper discusses the use of various scoring metrics in the Bayesian optimization algorithm BOA which uses Bayesian networks to model promising solutions and generate the new ones The use of decision graphs in Bayesian networks to improve the performance of the BOA is proposed To favor simple models a complexity measure is incorporated into the Bayesian Dirichlet metric for Bayesian networks with decision graphs The presented algorithms are compared on a number of interesting problems Recently the use of local structures as default tables and decision treesgraphs in context of learning the structure of Bayesian networks has been proposed and discussed Friedman Goldszmidt Chickering\t Heckerman\t Meek\t Using local structures has shown to improve the performance of methods for learning Bayesian networks in terms of the likelihood of the resulting