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

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

Relabeling and Summarizing Posterior Distributions in Signal Decomposition Problems when the Number of Components is Unknown

International audienceThis paper addresses the problems of relabeling and summarizing posterior distributions that typically arise, in a Bayesian framework, when dealing with signal decomposition problems with an unknown number of components. Such posterior distributions are defined over union of subspaces of differing dimensionality and can be sampled from using modern Monte Carlo techniques, for instance the increasingly popular RJ-MCMC method. No generic approach is available, however, to summarize the resulting variable-dimensional samples and extract from them component-specific parameters. We propose a novel approach, named Variable-dimensional Approximate Posterior for Relabeling and Summarizing (VAPoRS), to this problem, which consists in approximating the posterior distribution of interest by a "simple"---but still variable-dimensional---parametric distribution. The distance between the two distributions is measured using the Kullback-Leibler divergence, and a Stochastic EM-type algorithm, driven by the RJ-MCMC sampler, is proposed to estimate the parameters. Two signal decomposition problems are considered, to show the capability of VAPoRS both for relabeling and for summarizing variable dimensional posterior distributions: the classical problem of detecting and estimating sinusoids in white Gaussian noise on the one hand, and a particle counting problem motivated by the Pierre Auger project in astrophysics on the other hand

Summarizing Posterior Distributions in Signal Decomposition Problems when the Number of Components is Unknown

International audienceThis paper addresses the problem of summarizing the posterior distributions that typically arise, in a Bayesian framework, when dealing with signal decomposition problems with unknown number of components. Such posterior distributions are defined over union of subspaces of differing dimensionality and can be sampled from using modern Monte Carlo techniques, for instance the increasingly popular RJ-MCMC method. No generic approach is available, however, to summarize the resulting variable-dimensional samples and extract from them component-specific parameters. We propose a novel approach to this problem, which consists in approximating the complex posterior of interest by a "simple"--but still variable-dimensional--parametric distribution. The distance between the two distributions is measured using the Kullback- Leibler divergence, and a Stochastic EM-type algorithm, driven by the RJ-MCMC sampler, is proposed to estimate the parameters. The proposed algorithm is illustrated on the fundamental signal processing example of joint detection and estimation of sinusoids in white Gaussian noise

An empirical Bayes approach for joint Bayesian model selection and estimation of sinusoids via reversible jump MCMC

This paper addresses the sensitivity of the algorithm proposed by Andrieu and Doucet (IEEE Trans. Signal Process., 47(10), 1999), for the joint Bayesian model selection and estimation of sinusoids in white Gaussian noise, to the values of a certain hyperparameter claimed to be weakly influential in the original paper. A deeper study of this issue reveals indeed that the value of this hyperparameter (the scale parameter of the expected signal-to-noise ratio) has a significant influence on 1) the mixing rate of the Markov chain and 2) the posterior distribution of the number of components. As a possible workaround for this problem, we investigate an Empirical Bayes approach to select an appropriate value for this hyperparameter in a data-driven way. Marginal likelihood maximization is performed by means of an importance sampling based Monte Carlo EM (MCEM) algorithm. Numerical experiments illustrate that the sampler equipped with this MCEM procedure provides satisfactory performances in moderate to high SNR situations

A new approach for improving coronary plaque component analysis based on intravascular ultrasound images

Virtual histology intravascular ultrasound (VH-IVUS) is a clinically available technique for atherosclerosis plaque characterization. It, however, suffers from a poor longitudinal resolution due to electrocardiogram (ECG)-gated acquisition. This article presents an effective algorithm for IVUS image-based histology to overcome this limitation. After plaque area extraction within an input IVUS image, a textural analysis procedure consisting of feature extraction and classification steps is proposed. The pixels of the extracted plaque area excluding the shadow region were classified into one of the three plaque components of fibro-fatty (FF), calcification (CA) or necrotic core (NC) tissues. The average classification accuracy for pixel and region based validations is 75% and 87% respectively. Sensitivities (specificities) were 79% (85%) for CA, 81% (90%) for FF and 52% (82%) for NC. The kappa (kappa) = 0.61 and p value = 0.02 indicate good agreement of the proposed method with VH images. Finally, the enhancement in the longitudinal resolution was evaluated by reconstructing the IVUS images between the two sequential IVUS-VH images

Décomposition de signaux dans un cadre bayésien trans-dimensionnel

This thesis addresses the challenges encountered when dealing with signal decomposition problems with an unknown number of components in a Bayesian framework. Particularly, we focus on the issue of summarizing the variable-dimensional posterior distributions that typically arise in such problems. Such posterior distributions are defined over union of subspaces of differing dimensionality, and can be sampled from using modern Monte Carlo techniques, for instance the increasingly popular Reversible-Jump MCMC (RJ-MCMC) sampler. No generic approach is available, however, to summarize the resulting variable-dimensional samples and extract from them component-specific parameters. One of the main challenges that needs to be addressed to this end is the label-switching issue, which is caused by the invariance of the posterior distribution to the permutation of the components. We propose a novel approach to this problem, which consists in approximating the complex posterior of interest by a “simple”—but still variable-dimensional parametric distribution. We develop stochastic EM-type algorithms, driven by the RJ-MCMC sampler, to estimate the parameters of the model through the minimization of a divergence measure between the two distributions. Two signal decomposition problems are considered, to show the capability of the proposed approach both for relabeling and for summarizing variable dimensional posterior distributions: the classical problem of detecting and estimating sinusoids in white Gaussian noise on the one hand, and a particle counting problem motivated by the Pierre Auger project in astrophysics on the other hand.Cette thèse porte sur le problème de la décomposition de signaux contenant un nombre inconnu de composantes, envisagé dans un cadre bayésien. En particulier, nous nous concentrons sur la question de la description des lois a posteriori qui ont la spécificité, pour les problèmes de ce genre, d’être définies sur une union de sous-espaces de dimensions différentes. Ces lois peuvent être échantillonnées à l’aide de techniques de Monte Carlo récentes, telles que l’échantillonneur MCMC à sauts réversibles (RJ-MCMC), mais aucune approche générique n’existe à l’heure actuelle pour décrire les échantillons produits par un tel échantillonneur et en extraire les paramètres spécifiques des composantes. L’un des principaux obstacles est le problème de la commutation des étiquettes (label-switching), causé par l’invariance de la loi a posteriori vis-à-vis de permutations de ses composantes. Nous proposons une nouvelle approche pour résoudre ce problème, qui consiste à approcher la loi a posteriori d’intérêt par une loi paramétrique plus “simple”, mais toujours définie sur un espace de dimension variable. Nous développons des algorithmes de type SEM (Stochastic Expectation-Maximization), s’appuyant sur la sortie d’un échantillonneur RJ-MCMC, afin d’estimer les paramètres du modèle par minimisation d’une divergence entre les deux lois. Deux problèmes de décomposition de signaux illustrent la capacité de la méthode proposée à résoudre le problème de commutation des étiquettes et à produire des résumés de lois a posteriori définies sur des espaces de dimension variable : le problème classique de détection et d’estimation de composantes sinusoïdales dans un bruit blanc d’une part, et un problème de comptage de particules motivé par le projet Pierre Auger en astrophysique d’autre part

Comparison of Fully Bayesian and Empirical Bayes approaches for joint Bayesian model selection and estimation of sinusoids via reversible jump MCMC

This work addresses the sensitivity 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 white Gaussian noise, to the values of a certain hyperparameter claimed to be weakly influential in the original paper. On the basis of extensive numerical experiments, we argue on the contrary that the value of this hyperparameter (the scale parameter of the expected signal-to-noise ratio) has a strong influence on 1) the mixing rate of the Markov chain and 2) the posterior distribution of the number of components. Fully Bayesian and Empirical Bayes methods are proposed and compared for estimating an appropriate value for this hyperparameter. In the Fully Bayesian approach, a weakly informative proper prior is assigned over that hyperparameter. In the Empirical Bayes approach, marginal likelihood maximization is performed by means of an importance sampling-based Monte Carlo EM (MCEM) algorithm. The pros and cons of each method are discussed on the basis of numerical experiments conducted with several sample sizes and signal-to-noise ratios