Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria

by   Marko Järvenpää, et al.

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.


Accelerating ABC methods using Gaussian processes

Approximate Bayesian computation (ABC) methods are used to approximate p...

Bayesian Structural Identification using Gaussian Process Discrepancy Models

Bayesian model updating based on Gaussian Process (GP) models has receiv...

Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC

We present an efficient approach for doing approximate Bayesian inferenc...

Frequentist coverage and sup-norm convergence rate in Gaussian process regression

Gaussian process (GP) regression is a powerful interpolation technique d...

Distribution of Gaussian Process Arc Lengths

We present the first treatment of the arc length of the Gaussian Process...

Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times

Computing a Gaussian process (GP) posterior has a computational cost cub...

Please sign up or login with your details

Forgot password? Click here to reset