Work

Fully adaptive Gaussian mixture Metropolis-Hastings algorithm

Year: 2013

Type: conference-paper

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.

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp.2013.6638846

Authors: David Luengo, Luca Martino

Institutions: Universidad Politécnica de Madrid, Universidad Carlos III de Madrid

Language: English

Topic: Markov Chains and Monte Carlo Methods

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2069690729

FWCI: 11.54

Cites: 17

Cited by: 38

Related to: 10