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Efficient likelihood-free Bayesian Computation for household epidemics

Neal, Peter (2012) Efficient likelihood-free Bayesian Computation for household epidemics. Statistics and Computing, 22 (6). pp. 1239-1256. ISSN 0960-3174

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Considerable progress has been made in applying Markov chain Monte Carlo (MCMC) methods to the analysis of epidemic data. However, this likelihood based method can be inefficient due to the limited data available concerning an epidemic outbreak. This paper considers an alternative approach to studying epidemic data using Approximate Bayesian Computation (ABC) methodology. ABC is a simulation-based technique for obtaining an approximate sample from the posterior distribution of the parameters of the model and in an epidemic context is very easy to implement. A new approach to ABC is introduced which generates a set of values from the (approximate) posterior distribution of the parameters during each simulation rather than a single value. This is based upon coupling simulations with different sets of parameters and we call the resulting algorithm coupled ABC. The new methodology is used to analyse final size data for epidemics amongst communities partitioned into households. It is shown that for the epidemic data sets coupled ABC is more efficient than ABC and MCMC-ABC.

Item Type: Journal Article
Journal or Publication Title: Statistics and Computing
Uncontrolled Keywords: Approximate Bayesian Computation ; Household epidemic data ; Stochastic epidemic models
Subjects: ?? qa ??
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 59737
Deposited By: ep_importer_pure
Deposited On: 31 Oct 2012 11:04
Refereed?: Yes
Published?: Published
Last Modified: 11 Apr 2018 00:46
Identification Number:

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