Fully adaptive gaussian mixture metropolis-hastings algorithm

Luengo García, David and Martino, Luca (2013). Fully adaptive gaussian mixture metropolis-hastings algorithm. In: "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.

Description

Title: Fully adaptive gaussian mixture metropolis-hastings algorithm
Author/s:
  • Luengo García, David
  • Martino, Luca
Item Type: Presentation at Congress or Conference (Article)
Event Title: 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Event Dates: 26/05/2013 - 31/05/2013
Event Location: Vancouver (Canadá)
Title of Book: Proceedings of the 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)
Date: 2013
ISBN: 978-1-4799-0356-6
Subjects:
Freetext Keywords: Markov Chain Monte Carlo (MCMC), Gaussian mixtures, adaptive Metropolis-Hastings
Faculty: E.U.I.T. Telecomunicación (UPM)
Department: Ingeniería de Circuitos y Sistemas [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 33283
DC Identifier: http://oa.upm.es/33283/
OAI Identifier: oai:oa.upm.es:33283
Official URL: https://www2.securecms.com/ICASSP2013/default.asp
Deposited by: Memoria Investigacion
Deposited on: 15 Apr 2015 17:24
Last Modified: 15 Apr 2015 17:24
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