Distribution-Free Learning of Bayesian Network Structure in Continuous Domains
In this paper authors present a method for learning the structure of Bayesian networks (BNs) without making any assumptions on the probability distribution of the domain. This is mainly useful for continuous domains, where there is little guidance and many choices for the parametric distribution families to be used for the local conditional probabilities of the Bayesian network, and only a few have been examined analytically. Authors therefore focus on BN structure learning in continuous domains. Authors address the problem by developing a conditional independence test for continuous variables, which can be readily used by any existing independence-based BN structure learning algorithm. The test is non-parametric, making no assumptions on the distribution of the domain.