Rights statement: ‘This is the peer reviewed version of the following article: Neal, P., and Terry Huang, C. L. (2015), Forward Simulation Markov Chain Monte Carlo with Applications to Stochastic Epidemic Models. Scand J Statist, 42, 378–396. doi: 10.1111/sjos.12111. which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/sjos.12111/abstract. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving'.
Accepted author manuscript, 499 KB, PDF document
Rights statement: © 2014 The Authors. Scandinavian Journal of Statistics published by John Wiley & Sons Ltd on behalf of The Board of the Foundation of the Scandinavian Journal of Statistics. The copyright line of this article has been subsequently changed [30 March 2015]. This article is made OnlineOpen. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Final published version, 601 KB, PDF document
Available under license: CC BY
Final published version
Licence: CC BY
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Forward simulation MCMC with applications to stochastic epidemic models
AU - Neal, Peter
AU - Huang, Chien Lin
N1 - © 2014 The Authors. Scandinavian Journal of Statistics published by John Wiley & Sons Ltd on behalf of The Board of the Foundation of the Scandinavian Journal of Statistics. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. The copyright line of this article has been subsequently changed [30 March 2015]. This article is made OnlineOpen.
PY - 2015/6
Y1 - 2015/6
N2 - For many stochastic models, it is difficult to make inference about the model parameters because it is impossible to write down a tractable likelihood given the observed data. A common solution is data augmentation in a Markov chain Monte Carlo (MCMC) framework. However, there are statistical problems where this approach has proved infeasible but where simulation from the model is straightforward leading to the popularity of the approximate Bayesian computation algorithm. We introduce a forward simulation MCMC (fsMCMC) algorithm, which is primarily based upon simulation from the model. The fsMCMC algorithm formulates the simulation of the process explicitly as a data augmentation problem. By exploiting non-centred parameterizations, an efficient MCMC updating schema for the parameters and augmented data is introduced, whilst maintaining straightforward simulation from the model. The fsMCMC algorithm is successfully applied to two distinct epidemic models including a birth–death–mutation model that has only previously been analysed using approximate Bayesian computation methods.
AB - For many stochastic models, it is difficult to make inference about the model parameters because it is impossible to write down a tractable likelihood given the observed data. A common solution is data augmentation in a Markov chain Monte Carlo (MCMC) framework. However, there are statistical problems where this approach has proved infeasible but where simulation from the model is straightforward leading to the popularity of the approximate Bayesian computation algorithm. We introduce a forward simulation MCMC (fsMCMC) algorithm, which is primarily based upon simulation from the model. The fsMCMC algorithm formulates the simulation of the process explicitly as a data augmentation problem. By exploiting non-centred parameterizations, an efficient MCMC updating schema for the parameters and augmented data is introduced, whilst maintaining straightforward simulation from the model. The fsMCMC algorithm is successfully applied to two distinct epidemic models including a birth–death–mutation model that has only previously been analysed using approximate Bayesian computation methods.
KW - approximate Bayesian computation
KW - birth–death–mutation model
KW - importance sampling
KW - Markov chain Monte Carlo
KW - non-centred parameterization
KW - SIR and SIS epidemic models
U2 - 10.1111/sjos.12111
DO - 10.1111/sjos.12111
M3 - Journal article
VL - 42
SP - 378
EP - 396
JO - Scandinavian Journal of Statistics
JF - Scandinavian Journal of Statistics
SN - 0303-6898
IS - 2
ER -