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

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.
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Communication dans un congrès
21st International Conference on Computational Statistics (COMPSTAT 2014), Aug 2014, Genève, Switzerland. 〈http://compstat2014.org/〉
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https://hal.inria.fr/hal-01108399
Contributeur : Adrien Todeschini <>
Soumis le : jeudi 22 janvier 2015 - 16:21:05
Dernière modification le : lundi 15 janvier 2018 - 12:20:02

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  • HAL Id : hal-01108399, version 1

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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), Aug 2014, Genève, Switzerland. 〈http://compstat2014.org/〉. 〈hal-01108399〉

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