Séminaire de Probabilités et Statistique :

Le 22 avril 2024 à 13:45 - UM - Bât 09 - Salle de conférence (1er étage)

Présentée par Stoehr Julien - Université Paris Dauphine

Composite likelihood inference for the Poisson log-normal model

The Poisson log-normal model is a latent variable model that provides a generic framework for the analysis of multivariate count data. Inferring its parameters can be a daunting task since the conditional distribution of the latent variables given the observed ones is intractable. For this model, variational approaches are the golden standard solution as they prove to be computationally efficient but lack theoretical guarantees on the estimates. Sampling based solutions are quite the opposite. Starting from already available variational approximations, we define a first Monte Carlo EM algorithm to obtain maximum likelihood estimators. We then extend this algorithm to the case of a composite likelihood in order to be able to handle higher dimensional count data.

Séminaire en salle 109, également retransmis sur zoom : https://umontpellier-fr.zoom.us/j/94087408185