An automatic adaptive method to combine summary statistics in approximate Bayesian computation.

ABSTRACT: To infer the parameters of mechanistic models with intractable likelihoods, techniques such as approximate Bayesian computation (ABC) are increasingly being adopted. One of the main disadvantages of ABC in practical situations, however, is that parameter inference must generally rely on summary statistics of the data. This is particularly the case for problems involving high-dimensional data, such as biological imaging experiments. However, some summary statistics contain more information about parameters of interest than others, and it is not always clear how to weight their contributions within the ABC framework. We address this problem by developing an automatic, adaptive algorithm that chooses weights for each summary statistic. Our algorithm aims to maximize the distance between the p

SUBMITTER: Harrison JU 

PROVIDER: S-EPMC7410215 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature