Scientists often express their understanding of
\nthe world through a computationally demanding
\nsimulation program. Analyzing the posterior
\ndistribution of the parameters given observations
\n(the inverse problem) can be extremely challenging.
\nThe Approximate Bayesian Computation
\n(ABC) framework is the standard statistical
\ntool to handle these likelihood free problems,
\nbut they require a very large number of simulations.
\nIn this work we develop two new ABC
\nsampling algorithms that significantly reduce the
\nnumber of simulations necessary for posterior inference.
\nBoth algorithms use confidence estimates
\nfor the accept probability in the Metropolis
\nHastings step to adaptively choose the number
\nof necessary simulations. Our GPS-ABC algorithm
\nstores the information obtained from every
\nsimulation in a Gaussian process which acts as
\na surrogate function for the simulated statistics.
\nExperiments on a challenging realistic biological
\nproblem illustrate the potential of these algorithms.<\/p>\n","protected":false},"excerpt":{"rendered":"
Scientists often express their understanding of the world through a computationally demanding simulation program. Analyzing the posterior distribution of the parameters given observations (the inverse problem) can be extremely challenging. The Approximate Bayesian Computation (ABC) framework is the standard statistical tool to handle these likelihood free problems, but they require a very large number of […]<\/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-443760","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Uncertainty in Artificial 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