Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian
Models
Description
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).