Séance Séminaire

Séminaire de Probabilités et Statistique

Bayesian inference on a mixture model with spatial dependence

We introduce a new technique to select the number of components of a mixture model with spatial dependence. It consists in an estimation of the Integrated Completed Likelihood based on a Laplace's approximation and a new technique to deal with the normalizing constant intractability of the hidden Potts model. Our proposal is applied to a real satellite image.