Conditional Density Approximations with Mixtures of Polynomials

Gherardo Varando, Pedro L. López-Cruz, Thomas Dyhre Nielsen, Concha Bielza, Pedro Larrañga

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3 Citations (Scopus)
316 Downloads (Pure)

Abstract

Mixtures of polynomials (MoPs) are a non-parametric density estimation technique especially designed for hybrid Bayesian networks with continuous and discrete variables. Algorithms to learn one- and multi-dimensional (marginal) MoPs from data have recently been proposed. In this paper we introduce two methods for learning MoP approximations 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 and study the methods using data sampled from known parametric distributions, and we demonstrate their applicability by learning models based on real neuroscience data. Finally, we compare the performance of the proposed methods with an approach for learning mixtures of truncated basis functions (MoTBFs). The empirical results show that the proposed methods generally yield models that are comparable to or significantly better than those found using the MoTBF-based method.
Original languageEnglish
JournalInternational Journal of Intelligent Systems
Volume30
Issue number3
Pages (from-to)236-264
ISSN0884-8173
DOIs
Publication statusPublished - 2015

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