Venue: Lecture Theatre, Department of Statistics, 1 South Parks Road, Oxford, OX1 3TG
Time: 2:15
Speaker: Richard G Everitt, Statistical Experimental Laboratory, Department of Statistics, University of Oxford
Title: Computational methods for ABC model choice
Abstract: The "likelihood-free" methods referred to as approximate Bayesian computation (ABC) are widely used for performing approximate Bayesian inference in situations where the likelihood is intractable. ABC methods for model choice when the likelihood is intractable have also been studied, but the range of computational techniques that have been applied thus far in this situation is small compared to those used routinely on problems where the likelihood can be evaluated. In this talk we focus on using one particular class of algorithms for ABC model choice: those that jointly explore the joint space of models and their parameters. The methodology is applied to the problem of source term estimation.
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Christine Stone
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Department of Statistics, University of Oxford
1 South Parks Road
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