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López-Cruz, Pedro L., Nielsen, Thomas D., Bielza Lozoya, María Concepción ORCID: https://orcid.org/0000-0001-7109-2668 and Larrañaga Múgica, Pedro María ORCID: https://orcid.org/0000-0003-0652-9872 (2013). Learning mixtures of polynomials of conditional densities from data. En: "15th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2013)", 17-20 Sep 2013, Madrid, España. ISBN 978-3-642-40642-3. pp. 363-372. https://doi.org/10.1007/978-3-642-40643-0_37.
Título: | Learning mixtures of polynomials of conditional densities from data |
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Autor/es: |
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Tipo de Documento: | Ponencia en Congreso o Jornada (Artículo) |
Título del Evento: | 15th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2013) |
Fechas del Evento: | 17-20 Sep 2013 |
Lugar del Evento: | Madrid, España |
Título del Libro: | Advances in Artificial Intelligence |
Fecha: | 2013 |
ISBN: | 978-3-642-40642-3 |
Materias: | |
Palabras Clave Informales: | Hybrid Bayesian networks, Conditional density estimation, Mixtures of polynomials. |
Escuela: | E.T.S. de Ingenieros Informáticos (UPM) |
Departamento: | Inteligencia Artificial |
Licencias Creative Commons: | Reconocimiento - Sin obra derivada - No comercial |
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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.
ID de Registro: | 74122 |
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Identificador DC: | https://oa.upm.es/74122/ |
Identificador OAI: | oai:oa.upm.es:74122 |
Identificador DOI: | 10.1007/978-3-642-40643-0_37 |
URL Oficial: | https://link.springer.com/chapter/10.1007/978-3-64... |
Depositado por: | Biblioteca Facultad de Informatica |
Depositado el: | 13 Jun 2023 11:56 |
Ultima Modificación: | 20 Mar 2024 18:37 |