Skip to Main content Skip to Navigation
Conference papers

On Reparameterisations of the Poisson Process Model for Extremes in a Bayesian Framework

Abstract : Combining extreme value analysis with Bayesian methods has several advantages, such as the consideration of prior information or the ability to study irregular cases for frequentist statistics. We focus here on a model of extremes by Poisson process, and propose an alternative of a recent study on a parameterisation of the model which orthogonalizes the parameters to improve posterior sampling by Markov chain Monte-Carlo method (MCMC).
Document type :
Conference papers
Complete list of metadata

https://hal.inria.fr/hal-03264261
Contributor : Théo Moins Connect in order to contact the contributor
Submitted on : Friday, June 18, 2021 - 9:23:49 AM
Last modification on : Friday, February 4, 2022 - 3:30:27 AM
Long-term archiving on: : Sunday, September 19, 2021 - 6:15:15 PM

File

JDS___Processus_poisson_Bayes(...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03264261, version 1

Collections

Citation

Théo Moins, Julyan Arbel, Anne Dutfoy, Stéphane Girard. On Reparameterisations of the Poisson Process Model for Extremes in a Bayesian Framework. JDS 2021 - 52èmes Journées de Statistique de la Société Française de Statistique (SFdS), Jun 2021, Nice / Virtual, France. pp.1-6. ⟨hal-03264261⟩

Share

Metrics

Record views

36

Files downloads

52