Biips software: inference in Bayesian graphical models with sequential Monte Carlo methods
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| hal.structure.identifier | Quality control and dynamic reliability [CQFD] | |
| dc.contributor.author | TODESCHINI, Adrien | |
| hal.structure.identifier | Department of Statistics [Oxford] | |
| dc.contributor.author | CARON, Francois | |
| hal.structure.identifier | Service Expérimentation et Développement [Bordeaux] [SED] | |
| dc.contributor.author | FUENTES, Marc | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| hal.structure.identifier | Quality control and dynamic reliability [CQFD] | |
| dc.contributor.author | LEGRAND, Pierrick | |
| hal.structure.identifier | University of New South Wales [Sydney] [UNSW] | |
| dc.contributor.author | DEL MORAL, Pierre | |
| dc.date.accessioned | 2024-04-04T03:19:16Z | |
| dc.date.available | 2024-04-04T03:19:16Z | |
| dc.date.conference | 2014-08-19 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/194491 | |
| dc.description.abstractEn | 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. | |
| dc.language.iso | en | |
| dc.subject.en | Statistical software | |
| dc.subject.en | Bayesian Graphical Models | |
| dc.subject.en | Sequential Monte Carlo | |
| dc.subject.en | Particle Filtering | |
| dc.subject.en | BUGS | |
| dc.title.en | Biips software: inference in Bayesian graphical models with sequential Monte Carlo methods | |
| dc.type | Communication dans un congrès | |
| dc.subject.hal | Statistiques [stat]/Calcul [stat.CO] | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.conference.title | 21st International Conference on Computational Statistics (COMPSTAT 2014) | |
| bordeaux.country | CH | |
| bordeaux.conference.city | Genève | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-01108399 | |
| hal.version | 1 | |
| hal.invited | oui | |
| hal.proceedings | non | |
| hal.conference.organizer | European Regional Section of the IASC | |
| hal.conference.end | 2014-08-22 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-01108399v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=TODESCHINI,%20Adrien&CARON,%20Francois&FUENTES,%20Marc&LEGRAND,%20Pierrick&DEL%20MORAL,%20Pierre&rft.genre=unknown |
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