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Bayesian leave-one-out cross-validation for large data
Måns Magnusson · Michael Andersen · Johan Jonasson · Aki Vehtari

Tue Jun 11 04:20 PM -- 04:25 PM (PDT) @ Room 101

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.

Author Information

Måns Magnusson (Aalto University)
Michael Andersen (Aalto University)
Johan Jonasson (Chalmers University of Technology)
Aki Vehtari (Aalto University)

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