| Paper: | MLSP-L1.1 | ||
| Session: | Learning Theory I | ||
| Time: | Wednesday, May 17, 10:00 - 10:20 | ||
| Presentation: | Lecture | ||
| Topic: | Machine Learning for Signal Processing: Bayesian Learning and Modeling | ||
| Title: | Estimation of Mixtures of Symmetric Alpha Stable Distributions with an Unknown Number of Components | ||
| Authors: | Diego Salas-Gonzalez, University of Granada, Spain; Ercan Engin Kuruoglu, ISTI / CNR, Italy; Diego Pablo Ruiz Padillo, University of Granada, Spain | ||
| Abstract: | In this work, we study the estimation of mixtures of symmetric alpha-stable distributions using Bayesian inference. We utilise numerical Bayesian sampling techniques such as Markov chain Monte Carlo (MCMC). Our estimation technique is capable of estimating also the number of alpha-stable components in the mixture in addition to the component parameters and mixing coefficients which is accomplished by the use of the Reversible Jump MCMC algorithm. | ||
©2018 Conference Management Services, Inc. -||- email: webmaster@icassp2006.org -||- Last updated Friday, August 17, 2012