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Efficient model comparison techniques for models requiring large scale data augmentation. / Touloupou, Panayiota; Alzahrani, Naif; Neal, Peter John et al.
In: Bayesian Analysis, Vol. 13, No. 2, 03.2018, p. 437-459.Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Efficient model comparison techniques for models requiring large scale data augmentation
AU - Touloupou, Panayiota
AU - Alzahrani, Naif
AU - Neal, Peter John
AU - Spencer, Simon
AU - McKinley, Trevelyan
PY - 2018/3
Y1 - 2018/3
N2 - Selecting between competing statistical models is a challenging problem especially when the competing models are non-nested. In this paper we offer a simple solution by devising an algorithm which combines MCMC and importance sampling to obtain computationally efficient estimates of the marginal likelihood which can then be used to compare the models. The algorithm is successfully applied to a longitudinal epidemic data set, where calculating the marginal likelihood is made more challenging by the presence of large amounts of missing data. In this context, our importance sampling approach is shown to outperform existing methods for computing the marginal likelihood.
AB - Selecting between competing statistical models is a challenging problem especially when the competing models are non-nested. In this paper we offer a simple solution by devising an algorithm which combines MCMC and importance sampling to obtain computationally efficient estimates of the marginal likelihood which can then be used to compare the models. The algorithm is successfully applied to a longitudinal epidemic data set, where calculating the marginal likelihood is made more challenging by the presence of large amounts of missing data. In this context, our importance sampling approach is shown to outperform existing methods for computing the marginal likelihood.
KW - Epidemics
KW - marginal likelihood
KW - model evidence
KW - model selection
KW - time series
U2 - 10.1214/17-BA1057
DO - 10.1214/17-BA1057
M3 - Journal article
VL - 13
SP - 437
EP - 459
JO - Bayesian Analysis
JF - Bayesian Analysis
SN - 1936-0975
IS - 2
ER -