On the joint Bayesian model selection and estimation of sinusoids via reversible jump MCMC in low SNR situations - Archive ouverte HAL Access content directly
Conference Papers Year : 2010

On the joint Bayesian model selection and estimation of sinusoids via reversible jump MCMC in low SNR situations

Abstract

This paper addresses the behavior in low SNR situations of the algorithm proposed by Andrieu and Doucet (IEEE T. Signal Proces., 47(10), 1999) for the joint Bayesian model selection and estimation of sinusoids in Gaussian white noise. It is shown that the value of a certain hyperparameter, claimed to be weakly influential in the original paper, becomes in fact quite important in this context. This robustness issue is fixed by a suitable modification of the prior distribution, based on model selection considerations. Numerical experiments show that the resulting algorithm is more robust to the value of its hyperparameters.
Fichier principal
Vignette du fichier
ISSPA.pdf (160.85 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00517661 , version 1 (15-09-2010)

Identifiers

  • HAL Id : hal-00517661 , version 1

Cite

Alireza Roodaki, Julien Bect, Gilles Fleury. On the joint Bayesian model selection and estimation of sinusoids via reversible jump MCMC in low SNR situations. 10th International Conference on Information Sciences, Signal Processing and their Applications (ISSPA'10), May 2010, Kuala Lumpur, Malaysia. pp.5-8. ⟨hal-00517661⟩
124 View
245 Download

Share

Gmail Facebook Twitter LinkedIn More