Approximate Bayesian Computation (ABC) is a framework for performing
\nlikelihood-free posterior inference for simulation models. Stochastic Variational
\ninference (SVI) is an appealing alternative to the inefficient sampling approaches
\ncommonly used in ABC. However, SVI is highly sensitive to the variance of
\nthe gradient estimators, and this problem is exacerbated by approximating the
\nlikelihood. We draw upon recent advances in variance reduction for SVI [6][13]
\nand likelihood-free inference using deterministic simulations [12] to produce low
\nvariance gradient estimators of the variational lower-bound. By then exploiting
\nautomatic differentiation libraries [8] we can avoid nearly all model-specific derivations.
\nWe demonstrate performance on three problems and compare to existing
\nSVI algorithms. Our results demonstrate the correctness and efficiency of our
\nalgorithm.<\/p>\n","protected":false},"excerpt":{"rendered":"
Approximate Bayesian Computation (ABC) is a framework for performing likelihood-free posterior inference for simulation models. Stochastic Variational inference (SVI) is an appealing alternative to the inefficient sampling approaches commonly used in ABC. However, SVI is highly sensitive to the variance of the gradient estimators, and this problem is exacerbated by approximating the likelihood. We draw […]<\/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":[193724],"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-443853","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"arXiv","msr_edition":"arXiv","msr_affiliation":"","msr_published_date":"2016-06-28","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"arXiv:1606.08549","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"443856","msr_publicationurl":"https:\/\/arxiv.org\/abs\/1606.08549","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"auto_abc_1606.08549","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2017\/11\/auto_abc_1606.08549.pdf","id":443856,"label_id":0},{"type":"url","title":"https:\/\/arxiv.org\/abs\/1606.08549","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":0,"url":"https:\/\/arxiv.org\/abs\/1606.08549"}],"msr-author-ordering":[{"type":"text","value":"Alexander Moreno","user_id":0,"rest_url":false},{"type":"text","value":"Tameem Adel","user_id":0,"rest_url":false},{"type":"user_nicename","value":"edmeeds","user_id":37182,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=edmeeds"},{"type":"text","value":"James M. 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