%0 Journal Article %1 yao2018evaluating %A Yao, Yuling %A Vehtari, Aki %A Simpson, Daniel %A Gelman, Andrew %D 2018 %K bayesian variational %T Yes, but Did It Work?: Evaluating Variational Inference %U http://arxiv.org/abs/1802.02538 %X 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.

@article{yao2018evaluating, 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 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.}, added-at = {2020-02-13T03:12:29.000+0100}, author = {Yao, Yuling and Vehtari, Aki and Simpson, Daniel and Gelman, Andrew}, biburl = {https://www.bibsonomy.org/bibtex/2af628fe384081565ccdeca275c658632/kirk86}, description = {[1802.02538] Yes, but Did It Work?: Evaluating Variational Inference}, interhash = {511e52d110ea79995e62d76e4895bf78}, intrahash = {af628fe384081565ccdeca275c658632}, keywords = {bayesian variational}, note = {cite arxiv:1802.02538Comment: Appearing at International Conference on Machine Learning 2018}, timestamp = {2020-02-13T03:12:29.000+0100}, title = {Yes, but Did It Work?: Evaluating Variational Inference}, url = {http://arxiv.org/abs/1802.02538}, year = 2018 }