A Strategy for Bayesian Inference for Computationally Expensive Models with Application to the Estimation of Stem Cell Properties

Antony Overstall, David Woods

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Bayesian inference is considered for statistical models that depend on the evaluation of a computationally expensive computer code or simulator. For such situations, the number of evaluations of the likelihood function, and hence of the unnormalised posterior probability density function, is determined by the available computational resource and may be extremely limited. We present a new example of such a simulator that describes the properties of human embryonic stem cells using data from optical trapping experiments. This application is used to motivate a novel strategy for Bayesian inference which exploits a Gaussian process approximation of the simulator and allows computationally efficient Markov Chain Monte Carlo inference. The advantages of this strategy over previous methodology are that it is less reliant on the determination of tuning parameters and allows the application of model diagnostic procedures that require no additional evaluations of the simulator. We show the advantages of our method on synthetic examples and demonstrate its application on stem cell experiments.
Original languageEnglish
Pages (from-to)458-468
Number of pages11
JournalBiometrics
Volume69
Issue number2
Early online date19 Feb 2013
DOIs
Publication statusPublished - Jun 2013

Keywords

  • Gaussian processes; Markov Chain Monte Carlo; Optical trapping experiments; Simulators

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