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Fully adaptive gaussian mixture metropolis-hastings algorithm
Cita
Luengo García, David 
ORCID: https://orcid.org/0000-0001-7407-3630 and Martino, Luca
(2013).
Fully adaptive gaussian mixture metropolis-hastings algorithm.
En: "38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP)", 26/05/2013 - 31/05/2013, Vancouver (Canadá). ISBN 978-1-4799-0356-6. pp. 6148-6152.
ORCID: https://orcid.org/0000-0001-7407-3630 and Martino, Luca
(2013).
Fully adaptive gaussian mixture metropolis-hastings algorithm.
En: "38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP)", 26/05/2013 - 31/05/2013, Vancouver (Canadá). ISBN 978-1-4799-0356-6. pp. 6148-6152.
Descripción
| Título: | Fully adaptive gaussian mixture metropolis-hastings algorithm |
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| Autor/es: |
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| Tipo de Documento: | Ponencia en Congreso o Jornada (Artículo) |
| Título del Evento: | 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) |
| Fechas del Evento: | 26/05/2013 - 31/05/2013 |
| Lugar del Evento: | Vancouver (Canadá) |
| Título del Libro: | Proceedings of the 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013) |
| Fecha: | 2013 |
| ISBN: | 978-1-4799-0356-6 |
| Materias: | |
| ODS: | |
| Palabras Clave Informales: | Markov Chain Monte Carlo (MCMC), Gaussian mixtures, adaptive Metropolis-Hastings |
| Escuela: | E.U.I.T. Telecomunicación (UPM) [antigua denominación] |
| Departamento: | Ingeniería de Circuitos y Sistemas [hasta 2014] |
| Licencias Creative Commons: | Reconocimiento - Sin obra derivada - No comercial |
Resumen
Markov Chain Monte Carlo methods are widely used in signal processing and communications for statistical inference and stochastic optimization. In this work, we introduce an efficient adaptive Metropolis-Hastings algorithm to draw samples from generic multimodal and multidimensional target distributions. The proposal density is a mixture of Gaussian densities with all parameters (weights, mean vectors and covariance matrices) updated using all the previously generated samples applying simple recursive rules. Numerical results for the one and two-dimensional cases are provided.
Más información
| ID de Registro: | 33283 |
|---|---|
| Identificador DC: | https://oa.upm.es/33283/ |
| Identificador OAI: | oai:oa.upm.es:33283 |
| URL Oficial: | https://www2.securecms.com/ICASSP2013/default.asp |
| Depositado por: | Memoria Investigacion |
| Depositado el: | 15 Abr 2015 17:24 |
| Ultima Modificación: | 15 Abr 2015 17:24 |
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