Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian
Models
Description
Efficient approximate leave-one-out cross-validation (LOO)
for Bayesian models fit using Markov chain Monte Carlo. 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.