Bayesian inference often requires integrating some function with respect to a posterior
distribution. Monte Carlo methods are sampling algorithms that allow to compute these …

Inference and optimization algorithms usually have hyperparameters that require to be
tuned in order to achieve efficiency. We consider here different approaches to efficiently …

Reversible jump MCMC (RJ-MCMC) sampling techniques, which allow to jointly tackle
model selection and parameter estimation problems in a coherent Bayesian framework …

Reversible jump MCMC (RJ-MCMC) sampling techniques, which allow to jointly tackle
model selection and parameter estimation problems in a coherent Bayesian framework …

This paper addresses the problems of relabeling and summarizing posterior distributions
that typically arise, in a Bayesian framework, when dealing with signal decomposition …

Long version of the paper. This long version of the paper features an additional evaluated
method for Section 2.2 (AM with posterior reordering), examples of the behavior of AMOR on …

When targeting a distribution that is artificially invariant under some permutations, Markov
chain Monte Carlo (MCMC) algorithms face the label-switching problem, rendering marginal …

In machine learning (ML), the training process of an algorithm generally consists of two
nested loops: the outer loop iterates over hyperparameter values, while the inner loop …

We extend the maximum likelihood method for wideband direction of arrival (DOA)
estimation to the case of an unknown number of moving sources. The extension is nontrivial …

When targeting a distribution that is artificially invariant under some permutations, Markov
chain Monte Carlo (MCMC) algorithms face the label-switching problem, rendering marginal …