Yes, but Did It Work?: Evaluating Variational Inference

Aki Vehtari; Andrew Gelman; Daniel Simpson; Yuling Yao

arxiv: 1802.02538 · v2 · pith:S2PJFY6Pnew · submitted 2018-02-07 · 📊 stat.ML · stat.CO

Yes, but Did It Work?: Evaluating Variational Inference

Yuling Yao , Aki Vehtari , Daniel Simpson , Andrew Gelman This is my paper

classification 📊 stat.ML stat.CO

keywords variationalapproximationdiagnosticwhilealgorithmsalleviatealwaysassesses

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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 Pareto-smoothed importance sampling (PSIS) diagnostic gives a goodness of fit measurement for joint distributions, while simultaneously improving the error in the estimate. The variational simulation-based calibration (VSBC) assesses the average performance of point estimates.

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