Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian
Models
Description
Efficient approximate leave-one-out cross-validation (LOO)
using Pareto smoothed importance sampling (PSIS), a new procedure for
regularizing importance weights. As a byproduct of the calculations, we also
obtain approximate standard errors for estimated predictive errors, and for
the comparison of predictive errors between models. We also compute the
widely applicable information criterion (WAIC).