Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria
This work addresses computational bottlenecks in ABC for researchers in population genetics, offering incremental improvements in efficiency and accuracy for model fitting in fields like bacterial evolution.
The authors tackled the problem of reducing the computational cost of Approximate Bayesian Computation (ABC) by modeling discrepancies with Gaussian processes, finding that GP choice significantly affects posterior accuracy and proposing an automated selection method based on expected utility. They applied this to estimate horizontal gene transfer in bacteria, providing insights into population genetic events.
Approximate Bayesian computation (ABC) can be used for model fitting when the likelihood function is intractable but simulating from the model is feasible. However, even a single evaluation of a complex model may take several hours, limiting the number of model evaluations available. Modelling the discrepancy between the simulated and observed data using a Gaussian process (GP) can be used to reduce the number of model evaluations required by ABC, but the sensitivity of this approach to a specific GP formulation has not yet been thoroughly investigated. We begin with a comprehensive empirical evaluation of using GPs in ABC, including various transformations of the discrepancies and two novel GP formulations. Our results indicate the choice of GP may significantly affect the accuracy of the estimated posterior distribution. Selection of an appropriate GP model is thus important. We formulate expected utility to measure the accuracy of classifying discrepancies below or above the ABC threshold, and show that it can be used to automate the GP model selection step. Finally, based on the understanding gained with toy examples, we fit a population genetic model for bacteria, providing insight into horizontal gene transfer events within the population and from external origins.