Adaptive approximate Bayesian computation for complex models

Maxime Lenormand (1) , Franck Jabot (1) , Guillaume Deffuant (1)

1 UR LISC - Laboratoire d'ingénierie pour les systèmes complexes

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.

Mots clés: Complex model; Approximate Bayesian computation; Population Monte Carlo; Sequential Monte Carlo

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Maxime Lenormand, Franck Jabot, Guillaume Deffuant. Adaptive approximate Bayesian computation for complex models. Computational Statistics, 2013, 28 (6), pp.2777-2796. ⟨10.1007/s00180-013-0428-3⟩. ⟨hal-00638484v4⟩