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Abstract
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| 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 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 the methods using data sampled from a simple Gaussian Bayesian network. We study and compare the performance of these methods with the approach for learning mixtures of truncated basis functions from data. | |
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International
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No |
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Congress
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XV Conferencia de la Asociación Española para la Inteligencia Artificial |
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960 |
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Place
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Madrid |
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Reviewers
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Si |
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ISBN/ISSN
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978-3-642-40642-3 |
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10.1007/978-3-642-40643-0_37 |
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Start Date
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17/09/2013 |
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End Date
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20/09/2013 |
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From page
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363 |
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To page
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372 |
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Advances in Artificial Intelligence, Proceedings of the 15th MultiConference of the Spanish Association for Artificial Intelligence, volume 8109 of Lecture Notes in Computer Science |