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) < doi:10.1007/s11222-016-9696-4 >. The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights.

loo is an R package that allows users to compute efficient approximate leave-one-out cross-validation for fitted Bayesian models, as well as model weights that can be used to average predictive distributions. The loo package package implements the fast and stable computations for approximate LOO-CV and WAIC from

The loo() methods for arrays, matrices, and functions compute PSIS-LOO CV, efficient approximate leave-one-out (LOO) cross-validation for Bayesian models using Pareto smoothed importance sampling (PSIS). This is an implementation of the methods described in Vehtari, Gelman, and Gabry (2017) and Vehtari, Simpson, Gelman, Yao, and Gabry (2024).

loo is an R package that allows users to compute efficient approximate leave-one-out cross-validation for fitted Bayesian models, as well as model weights that can be used to average predictive distributions. The loo package package implements the fast and stable computations for approximate LOO-CV and WAIC from

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 …

2024年7月4日 · 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) <doi:10.1007/s11222-016-9696-4>. The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights.

Leave-one-out cross-validation (LOO-CV) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-likelihood evaluated at the posterior simulations of the parameter values.

loo R package for approximate leave-one-out cross-validation (LOO-CV) and Pareto smoothed importance sampling (PSIS) - stan-dev/loo

This vignette demonstrates how to use the loo package to carry out Pareto smoothed importance-sampling leave-one-out cross-validation (PSIS-LOO) for purposes of model checking and model comparison.

For the `loo.function()` method and the `loo_i()` #' function, these are the data, posterior draws, and other arguments to pass #' to the log-likelihood function.