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

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Järvenpää, Marko Gutmann, Michael U. Vehtari, A. K.I. Marttinen, Pekka 2018-12-21T10:28:20Z 2018-12-21T10:28:20Z 2018-12-01
dc.identifier.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 . DOI: 10.1214/18-AOAS1150 en
dc.identifier.issn 1932-6157
dc.identifier.other PURE UUID: 0e1b26e9-9cc1-40b9-84f8-2a2e52c9e580
dc.identifier.other PURE ITEMURL:
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dc.description.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. en
dc.format.extent 24
dc.format.extent 2228-2251
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries Annals of Applied Statistics en
dc.relation.ispartofseries Volume 12, issue 4 en
dc.rights openAccess en
dc.subject.other Statistics and Probability en
dc.subject.other Modelling and Simulation en
dc.subject.other Statistics, Probability and Uncertainty en
dc.subject.other 113 Computer and information sciences en
dc.title Gaussian process modelling in approximate bayesian computation to estimate horizontal gene transfer in Bacteria en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Probabilistic Machine Learning
dc.contributor.department University of Edinburgh
dc.contributor.department Professorship Marttinen P.
dc.contributor.department Department of Computer Science en
dc.subject.keyword Approximate bayesian computation
dc.subject.keyword Gaussian process
dc.subject.keyword Input-dependent noise
dc.subject.keyword Intractable likelihood
dc.subject.keyword Model selection
dc.subject.keyword Statistics and Probability
dc.subject.keyword Modelling and Simulation
dc.subject.keyword Statistics, Probability and Uncertainty
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201812216620
dc.identifier.doi 10.1214/18-AOAS1150
dc.type.version publishedVersion

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