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| HAL : hal-00638484, version 3 |
| arXiv : 1111.1308 |
| Fiche détaillée |  Récupérer au format |
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| Versions disponibles : | v1 (05-11-2011) | v2 (15-03-2012) | v3 (31-07-2012) |
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| Adaptive approximate Bayesian computation for complex models |
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| Maxime Lenormand 1Franck Jabot 1 |
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| (04/11/2011) |
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| Approximate Bayesian computation (ABC) is a family of computational techniques in Bayesian statistics. These techniques allow to fi t a model to data without relying on the computation of the model likelihood. They instead require to simulate a large number of times the model to be fi tted. A number of re finements to the original rejection-based ABC scheme have been proposed, including the sequential improvement of posterior distributions. This technique allows to de- crease the number of model simulations required, but it still presents several shortcomings which are particu- larly problematic for costly to simulate complex models. We here provide a new algorithm to perform adaptive approximate Bayesian computation, which is shown to perform better on both a toy example and a complex social model. |
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| 1 : | Laboratoire d'ingénierie pour les systèmes complexes (UR LISC) |
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CEMAGREF |
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| Domaine | : | Mathématiques/Statistiques Statistiques/Théorie |
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| Approximate Bayesian computation – Population Monte Carlo – Sequential Monte Carlo – Complex model |
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| Liste des fichiers attachés à ce document : | |||||
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| hal-00638484, version 3 | |
| http://hal.archives-ouvertes.fr/hal-00638484 | |
| oai:hal.archives-ouvertes.fr:hal-00638484 | |
| Contributeur : Maxime Lenormand | |
| Soumis le : Mardi 31 Juillet 2012, 08:15:16 | |
| Dernière modification le : Mardi 31 Juillet 2012, 08:46:15 | |
