HAL - INRIA :: [hal-00517661, version 1] On the joint Bayesian model selection and estimation of sinusoids via reversible jump MCMC in low SNR situations

hal-00517661, version 1

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

Alireza Roodaki ¹, Julien Bect (, ) ^1, 2, Gilles Fleury ¹

10th International Conference on Information Sciences, Signal Processing and their Applications (ISSPA'10) (2010) 5-8

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.

1: Supélec Sciences des Systèmes - EA4454 (E3S)
SUPELEC
2: GdR MASCOT-NUM ((Méthodes d'Analyse Stochastique des Codes et Traitements Numériques))
CNRS : GDR3179

hal-00517661, version 1
http://hal-supelec.archives-ouvertes.fr/hal-00517661
oai:hal-supelec.archives-ouvertes.fr:hal-00517661
From: Karine El Rassi <>
Submitted on: Wednesday, 15 September 2010 10:34:57
Updated on: Wednesday, 15 September 2010 11:11:39

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