Memorias de investigación
Communications at congresses:
Fully Adaptive Gaussian Mixture Metropolis-Hastings Algorithm
Year:2013

Research Areas
  • Stochastic procedures of inference,
  • Estimators and predictors

Information
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.
International
Si
Congress
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
960
Place
Vancouver (Canadá)
Reviewers
Si
ISBN/ISSN
978-1-4799-0356-6
Start Date
26/05/2013
End Date
31/05/2013
From page
6148
To page
6152
Proceedings of the 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing
Participants

Research Group, Departaments and Institutes related
  • Creador: Departamento: Ingeniería de Circuitos y Sistemas