Bayesian leave-one-out cross-validation for large data

Model inference, such as model comparison, model checking, and model selection, is an important part of model development. Leave-one-out cross-validation (LOO-CV) is a general approach for assessing the generalizability of a model, but unfortunately, LOO-CV does not scale well to large datasets. We propose a combination of using approximate inference techniques and probability-proportional-to-size-sampling (PPS) for fast LOO-CV model evaluation for large data. We provide both theoretical and empirical results showing good properties for large data.