Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many statistical models can be simulated forwards but have intractable likelihoods. Approximate Bayesian Computation (ABC) methods are used to infer properties of these models from data. Traditionally these methods approximate the posterior over parameters by conditioning on data being inside an -ball around the observed data, which is only correct in the limit !0. Monte Carlo methods can then draw samples from the approximate posterior to approximate predictions or error bars on parameters. These algorithms critically slow down as !0, and in practice draw samples from a broader distribution than the posterior. We propose a new approach to likelihood-free inference based on Bayesian conditional density estimation. Preliminary inferences based on limited simulation data are used to guide later simulations. In some cases, learning an accurate parametric representation of the entire true posterior distribution requires fewer model simulations than Monte Carlo ABC methods need to produce a single sample from an approximate posterior.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 29 (NIPS 2016)
Place of PublicationBarcelona, Spain
PublisherNeural Information Processing Systems Foundation, Inc
Pages1028-1036
Number of pages9
Publication statusPublished - 10 Dec 2016
Event30th Annual Conference on Neural Information Processing Systems - Barcelona, Spain
Duration: 5 Dec 201610 Dec 2016
https://nips.cc/Conferences/2016

Publication series

NameAdvances in Neural Information Processing Systems
Volume29
ISSN (Electronic)1049-5258

Conference

Conference30th Annual Conference on Neural Information Processing Systems
Abbreviated titleNIPS 2016
Country/TerritorySpain
CityBarcelona
Period5/12/1610/12/16
Internet address

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