Bayesian leave-one-out cross-validation for large data
Paper i proceeding, 2019

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.


Mans Magnusson


Michael Riis Andersen

Danmarks Tekniske Universitet (DTU)


Johan Jonasson

Chalmers, Matematiska vetenskaper, Analys och sannolikhetsteori

Göteborgs universitet

Aki Vehtari


36th International Conference on Machine Learning, ICML 2019

Vol. 2019-June 7505-7525

36th International Conference on Machine Learning, ICML 2019
Long Beach, USA,


Teknisk mekanik

Bioinformatik (beräkningsbiologi)

Sannolikhetsteori och statistik

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