Towards not being afraid of the big bad data set

This talk will present the foundations behind a new algorithm for systematic error-free Monte Carlo simulation from intractable target distributions. The main motivation behind the work is to construct a method for exploring posterior distributions for Bayesian analyses of extremely large datasets where computation of the likelihood function at each iteration of an algorithm is prohibitively expensive. The algorithm is a continuous time sequential Monte Carlo procedure which extends many of the ideas used in exact simulation from diffusion sample paths.Non UBCUnreviewedAuthor affiliation: University of WarwickFacult