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We propose a new algorithm based on a combination of an SMC sampler for estimating the ABC likelihood in the case of high-dimensional data (Prangle et al, 2018) and ABC-SMC for exploring the parameter space. The new method has a similar structure to SMC2 (Chopin et al, 2012). To automate the approach, we make use of an adaptive scheme for both the sequence of ABC tolerances in the SMC, and also for the MCMC rejuvenation steps of the external parameter space SMC. This is joint work with Ivis Kerama and Tom Thorne.
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Richard Everitt
Richard Everitt - is an Associate Professor in Statistics at the University of Warwick (UK). His research is in methodology for Bayesian computation, applied to problems in statistical genetics, neuroscience, ecology, weather and climate, spatial statistics, network analysis and signal processing.