Efficient model comparison techniques for models requiring large scale data augmentation
Venue: G.108, Alan Turing, The University of Manchester
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Abstract: Selecting between competing statistical models is a challenging problem especially when the competing models are non-nested. We offer a simple solution by devising an algorithm which combines MCMC and importance sampling to obtain computationally efficient estimates of the marginal likelihood (model evidence) which can then be used to compare the models. The incorporation of a particle filter step into the algorithm allows the algorithm to be successfully applied to time series data sets, where calculating the marginal likelihood is made more challenging by the presence of large amounts of missing data.