Gaussian process modelling in approximate bayesian computation to estimate horizontal gene transfer in Bacteria

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openAccess

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Volume Title

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Date

2018-12-01

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Mcode

Degree programme

Language

en

Pages

24
2228-2251

Series

Annals of Applied Statistics, Volume 12, issue 4

Abstract

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 itcan 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.

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Keywords

Approximate bayesian computation, Gaussian process, Input-dependent noise, Intractable likelihood, Model selection

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Citation

Järvenpää , M , Gutmann , M U , Vehtari , A K I & Marttinen , P 2018 , ' Gaussian process modelling in approximate bayesian computation to estimate horizontal gene transfer in Bacteria ' , Annals of Applied Statistics , vol. 12 , no. 4 , pp. 2228-2251 . https://doi.org/10.1214/18-AOAS1150