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

Jonathan U Harrison, Ruth E Baker

Published 2017-03-07Version 1

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 prior and the approximate posterior by automatically adapting the weights within the ABC distance function. To demonstrate the effectiveness of our algorithm, we apply it to several stochastic models of biochemical reaction networks, and a spatial model of diffusion, and compare our results with existing algorithms.