Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC

Leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-likelihood evaluated at the posterior simulations of the parameter values. LOO and WAIC have v...

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Veröffentlicht in:	Statistics and computing Jg. 27; H. 5; S. 1413 - 1432
Hauptverfasser:	Vehtari, Aki, Gelman, Andrew, Gabry, Jonah
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.09.2017 Springer Nature B.V
Schlagworte:	Artificial Intelligence Bayesian analysis Errors Importance sampling Mathematics and Statistics Probability and Statistics in Computer Science Statistical inference Statistical Theory and Methods Statistics Statistics and Computing/Statistics Programs Stan Widely applicable information criterion (WAIC) Bayesian computation Pareto smoothed importance sampling (PSIS) Leave-one-out cross-validation (LOO) fold cross-validation
ISSN:	0960-3174, 1573-1375
Online-Zugang:	Volltext
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