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

Adrien Todeschini 1, 2 Francois Caron 3 Marc Fuentes 4 Pierrick Legrand 1, 2 Pierre del Moral 5
1 IMB - Institut de Mathématiques de Bordeaux
2 CQFD - Quality control and dynamic reliability
IMB - Institut de Mathématiques de Bordeaux, Inria Bordeaux - Sud-Ouest
3 Department of Statistics [Oxford]
4 SED - Service Expérimentation et Développement [Bordeaux]
5 UNSW - University of New South Wales [Sydney]
Abstract : 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.
Keywords : BUGS Particle Filtering Sequential Monte Carlo Bayesian Graphical Models Statistical software
Document type :
Conference papers
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https://hal.inria.fr/hal-01108399
Contributor : Adrien Todeschini <>
Submitted on : Thursday, January 22, 2015 - 4:21:05 PM
Last modification on : Thursday, May 2, 2019 - 2:10:05 PM

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Adrien Todeschini, Francois Caron, Marc Fuentes, Pierrick Legrand, Pierre del Moral. Biips software: inference in Bayesian graphical models with sequential Monte Carlo methods. 21st International Conference on Computational Statistics (COMPSTAT 2014), European Regional Section of the IASC, Aug 2014, Genève, Switzerland. ⟨hal-01108399⟩

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