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10 items by relevance
1 Preprint article

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

Magnusson, Måns; Andersen, Michael Riis; Jonasson, Johan; Vehtari, Aki

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

Year: 2019

Language: Undetermined

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2 Conference paper

Bayesian Leave-One-Out Cross-Validation for Large Data

Andersen, Michael Riis; Magnusson, Mans; Jonasson, Johan; Vehtari, Aki

Proceedings of the 36<sup>th</sup> International Conference on Machine Learning — 2019, pp. 7505-7525

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

Year: 2019

Language: English

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3 Book chapter

Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data

Magnusson, Måns; Andersen, Michael Riis; Jonasson, Johan; Vehtari, Aki

Proceedings of Machine Learning Research — 2020, pp. 341-350

Recently, new methods for model assessment, based on subsampling and posterior approximations, have been proposed for scaling leave-one-out cross-validation (LOO) to large datasets. Although these methods work well for estimating predictive performance for individual models, they are less powerful

Year: 2020

Language: English

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4 Preprint article

Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data

Magnusson, Måns; Andersen, Michael Riis; Jonasson, Johan; Vehtari, Aki

Recently, new methods for model assessment, based on subsampling and posterior approximations, have been proposed for scaling leave-one-out cross-validation (LOO) to large datasets. Although these methods work well for estimating predictive performance for individual models, they are less powerful

Year: 2020

Language: Undetermined

More...
oajicn bhl ekdpgmf

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