Bayesian leave-one-out cross-validation for large data
This addresses a scalability issue in model inference for researchers and practitioners working with large datasets, but it is incremental as it builds on existing LOO and sampling methods.
The paper tackled the problem of leave-one-out cross-validation (LOO) not scaling well to large datasets by proposing a combination of approximate inference techniques and probability-proportional-to-size-sampling (PPS) for fast LOO model evaluation, with results showing good theoretical and empirical properties 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) is a general approach for assessing the generalizability of a model, but unfortunately, LOO 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 model evaluation for large datasets. We provide both theoretical and empirical results showing good properties for large data.