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Cited by 357

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

loo: Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models

Open access
Vehtari, Aki, Gabry, Jonah, Magnusson, Måns, Yao, Yuling, Bürkner, Paul-Christian, Paananen, Topi, Gelman, Andrew·2015·CRAN: Contributed Packages

Abstract

Efficient approximate leave-one-out cross-validation (LOO) for Bayesian models fit using Markov chain Monte Carlo, as described in Vehtari, Gelman, and Gabry (2017) <doi:10.1007/s11222-016-9696-4>. The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of the calculations, we also obtain approximate standard errors for estimated predictive errors and for the comparison of predictive errors between models. The package also provides methods for using stacking and other model weighting techniques to average Bayesian predictive distributions.

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Metadata

Authors
Vehtari, A., Gabry, J., Magnusson, M., Yao, Y., Bürkner, P.-C., Paananen, T., & Gelman, A.
Venue
CRAN: Contributed Packages
Published
2015-06-26
OpenAlex
W4399648958
Cited by
357
References
3
Type
dataset
Language
en
Open access
gold

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