Yes, but did it work? Evaluating variational inference

Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

24 Citations (Scopus)

Abstract

While it's always possible to compute a variational approximation to a posterior distribution, it can be difficult to discover problems with this approximation". We propose two diagnostic algorithms to alleviate this problem. The Paretosmoothed importance sampling (PSIS) diagnostic gives a goodness of fit measurement for joint distributions, while simultaneously improving the error in the estimate. The variational simulationbased calibration (VSBC) assesses the average performance of point estimates.

Original languageEnglish
Title of host publicationProceedings of the 35th International Conference on Machine Learning
EditorsJennifer Dy, Andreas Krause
PublisherInternational Machine Learning Society (IMLS)
Pages8887-8895
Number of pages9
ISBN (Electronic)9781510867963
Publication statusPublished - 2018
Externally publishedYes
EventInternational Conference on Machine Learning 2018 - Stockholmsmässan, Stockholm, Sweden
Duration: 10 Jul 201815 Jul 2018
Conference number: 35th

Conference

ConferenceInternational Conference on Machine Learning 2018
Abbreviated titleICML 2018
Country/TerritorySweden
CityStockholm
Period10/07/1815/07/18

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