A New Approach for Hybrid Bayesian Networks Using Full Densities
Oliver C. Schrempf, Uwe D. Hanebeck · Repository KITopen (Karlsruhe Institute of Technology) · 2004
In this article, a new mechanism is described for modeling and evaluating hybrid Bayesian networks.The approach uses Gaussian mixtures and Dirac mixtures as messages to calculate marginal densities.The mechanism is proven to be exact, hence the accuracy of resulting marginals is only dependending on the accuracy of the conditional densities.As these densities are approximated by means of Gaussian mixtures, any desired precision can be achieved.The presented approach removes the restrictions concerning the ancestry of discrete nodes often made in literature.Hence it enables the designer to model arbitrary parent-child relationships using continuous and discrete variables.