Accueil > Activités > Séminaires > Probabilités et Statistique

Planning des séminaires 2022-2023

par Duchamps Jean-Jil - publié le , mis à jour le

Le séminaire a lieu le lundi, à 11h, a priori en salle 316 du bâtiment de Métrologie B (variable cette année pour cause de travaux). Vous trouverez ci-dessous le planning du séminaire de Probabilités-Statistique pour l’année universitaire en cours.

Contacts : jean-jil.duchamps univ-fcomte.fr ou yacouba.boubacar_mainassara univ-fcomte.fr

Exposés à venir :

12 décembre : Ben Taylor

Inference for aggregated spatiotemporal log-Gaussian Cox processes under changing and uncertain support

Abstract :
Aggregated point processes data are common in epidemiological applications. They arise when the true disease process is continuous in space-time, but only data from aggregation units, e.g. health facilities, or administrative regions, are available. The challenges posed by such data are often ignored, or substantially simplified in practice. In this talk, I will introduce solutions to the pragmatic challenges typically encountered through an example concerning the modelling of case counts of malaria at the health facility level in Zambia. Health facilities in Zambia have fuzzy catchment areas, they report irregularly and change in number and size over time. We treat the underlying data-generation process as a spatio-temporally continuous point process, capturing aggregation through an additional model hierarchy and using a GPU-accelerated data-augmentation scheme for inference. Along the way, I will share my thoughts on the ecological fallacy.

30 janvier : Estelle Medous


Abstract :

Exposés passés :

28 novembre : Théo Moins
(INRIA Grenoble)

Reparameterization of extreme value framework for improved Bayesian workflow

Abstract :
Combining extreme value theory with Bayesian methods offers several advantages, such as a quantification of uncertainty on parameter estimation or the ability to study irregular models that cannot be handled by frequentist statistics. However, it comes with many options that are left to the user concerning model building, computational algorithms, and even inference itself. Among them, the parameterization of the model induces a geometry that can alter the efficiency of computational algorithms, in addition to making calculations involved. We focus on the Poisson process characterization of extremes and outline two key benefits of an orthogonal parameterization addressing both issues. First, several diagnostics show that Markov chain Monte Carlo convergence is improved compared with the original parameterization. Second, orthogonalization also helps deriving Jeffreys and penalized complexity priors, and establishing posterior propriety. The analysis is supported by simulations, and our framework is then applied to extreme level estimation on river flow data.