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 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. Note: this work is currently under review in a journal and a full-length version is available as a non-refereed pre-print (https://arxiv.org/abs/1610.06462).
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. Note: this work is currently under review in a journal and a full-length version is available as a non-refereed pre-print (https://arxiv.org/abs/1610.06462).
| Original language | English |
|---|---|
| Type | Extended abstract in NIPS workshop on machine learning in computational biology |
| Publication status | Published - Dec 2017 |
| MoE publication type | Not Eligible |