Séminaire MODAL'X : Yiye Jiang (INRIA Grenoble Alpes)

Résumé : In this talk, we first introduce a new Bayesian model for inferring covariance and correlation matrices. In particular, we present double effects of the prior: information integration and regularization. After the model conception, a key challenge is sampling from the posterior distribution. Motivated by the related inference problem, we develop a general sampling algorithm that can also be applied in other settings. In the second part, we present the main principles of the algorithm, based on importance sampling. In the final part, we apply the proposed methodological and computational approach to a real fMRI dataset from rat brains. The inferred correlations among the MRI signals reveal the rats’ functional connectivity network.