Adaptive approximate Bayesian computation for complex models

TL;DR

This paper introduces an adaptive ABC algorithm that improves efficiency in fitting complex models by reducing the number of simulations needed, outperforming existing methods on toy and social models.

Contribution

A novel adaptive ABC algorithm that enhances efficiency and accuracy for complex models, addressing limitations of previous sequential methods.

Findings

Performs better on toy example

Outperforms existing methods on social model

Reduces number of simulations required

Abstract

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.