TODESCHINI, Adrien; CARON, Francois; FUENTES, Marc; LEGRAND, Pierrick; DEL MORAL, Pierre

Métadonnées

Afficher la notice complète

Licence d’utilisation du document

TODESCHINI, Adrien
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]

CARON, Francois
Department of Statistics [Oxford]

FUENTES, Marc
Service Expérimentation et Développement [Bordeaux] [SED]

Langue

Communication dans un congrès

Ce document a été publié dans

21st International Conference on Computational Statistics (COMPSTAT 2014), 2014-08-19, Genève.

Résumé en anglais

The main factor in the success of Markov Chain Monte Carlo Methods is that they can be implemented with little efforts in a large variety of settings. Many softwares have been developped such as BUGS and JAGS, that helped to popularize Bayesian methods. These softwares allow the user to define his statistical model in a so-called BUGS language, then runs MCMC algorithms as a black box.Although sequential Monte Carlo methods have become a very popular class of numerical methods over the last 20 years, there is no such “black box software” for this class of methods. The BiiPS software, which stands for Bayesian Inference with Interacting Particle Systems, aims at bridging this gap. From a graphical model defined in BUGS language, it automatically implements sequential Monte Carlo algorithms and provides summaries of the posterior distributions. In this poster, we will highlight some of the features of the BiiPS software and an illustration of its application to a stochastic volatility model.< Réduire

Mots clés en anglais

Statistical software

Bayesian Graphical Models

Sequential Monte Carlo

Particle Filtering

BUGS

Métadonnées

Partager cette publication !

Licence d’utilisation du document

Biips software: inference in Bayesian graphical models with sequential Monte Carlo methods

Langue

Ce document a été publié dans

Résumé en anglais

Mots clés en anglais

URI

Origine

Unités de recherche