We efficiently approximate leave-one-out cross-validation (LOO) using Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of our calculations, we also obtain approximate standard errors for estimated predictive errors, and for the comparison of predictive errors between two models. We also compute the widely applicable information criterion (WAIC).
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Summary
We efficiently approximate leave-one-out cross-validation (LOO) using Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of our calculations, we also obtain approximate standard errors for estimated predictive errors, and for the comparison of predictive errors between two models. We also compute the widely applicable information criterion (WAIC).
Last Updated
Apr 29, 2016 at 08:28
License
GPL (>= 3)
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433
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