We describe an embarrassingly parallel, anytime Monte Carlo method for
\nlikelihood-free models. The algorithm starts with the view that the stochasticity
\nof the pseudo-samples generated by the simulator can be controlled externally
\nby a vector of random numbers u, in such a way that the outcome, knowing u,
\nis deterministic. For each instantiation of u we run an optimization procedure to
\nminimize the distance between summary statistics of the simulator and the data.
\nAfter reweighing these samples using the prior and the Jacobian (accounting for
\nthe change of volume in transforming from the space of summary statistics to the
\nspace of parameters) we show that this weighted ensemble represents a Monte
\nCarlo estimate of the posterior distribution. The procedure can be run embarrassingly
\nparallel (each node handling one sample) and anytime (by allocating
\nresources to the worst performing sample). The procedure is validated on six experiments.<\/p>\n","protected":false},"excerpt":{"rendered":"
We describe an embarrassingly parallel, anytime Monte Carlo method for likelihood-free models. The algorithm starts with the view that the stochasticity of the pseudo-samples generated by the simulator can be controlled externally by a vector of random numbers u, in such a way that the outcome, knowing u, is deterministic. For each instantiation of u […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-443778","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Neural Information Processing Systems 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