Mixtures of polynomials (MoPs) are a non-parametric density estimation technique for hybrid Bayesian networks with continuous and discrete variables. We propose two methods for learning MoP ap- proximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the conditioning variables, but they differ as to how the MoP approximation of the quotient of the two densities is found. We illustrate the methods using data sampled from a simple Gaussian Bayesian network. We study and compare the performance of these meth- ods with the approach for learning mixtures of truncated basis functions from data.
Lecture Notes in Computer Science: 15th Conference of the Spanish Association for Artificial Intelligence, Caepia 2013, Madrid, Spain, September 17-20, 2013. Proceedings, 2013, p. 363-372
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Lecture Notes in Artificial Intelligence : Subseries of Lecture Notes in Computer Science
15th Conference of the Spanish Association for Artificial Intelligence, 2013