Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems

TL;DR

This paper introduces an ABC SMC method for parameter inference and model selection in dynamical systems, demonstrating improved performance and applicability to biological models without requiring likelihood calculations.

Contribution

The paper presents a novel ABC SMC algorithm that enhances parameter inference and model selection in dynamical systems, outperforming existing ABC methods.

Findings

ABC SMC provides insights into parameter inferability and model sensitivity.

The method successfully infers parameters and credible intervals in biological systems.

ABC SMC effectively selects the best model among alternatives.

Abstract

Approximate Bayesian computation methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper we discuss and apply an approximate Bayesian computation (ABC) method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC SMC gives information about the inferability of parameters and model sensitivity to changes in parameters, and tends to perform better than other ABC approaches. The algorithm is applied to several well known biological systems, for which parameters and their credible intervals are inferred. Moreover, we develop ABC SMC as a tool for model selection; given a range of different mathematical descriptions, ABC SMC is able to choose the best model using the standard Bayesian model selection apparatus.