Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian
Models
Efficient approximate leave-one-out cross-validation (LOO)
for Bayesian models fit using Markov chain Monte Carlo, as described
in Vehtari, Gelman, and Gabry (2017) .
The approximation uses 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. The package also provides methods for using
stacking and other model weighting techniques to average Bayesian
predictive distributions.