loo-package: Efficient LOO-CV and WAIC for Bayesian models
Description
Stan Development Team
This package implements the methods described in Vehtari, Gelman, and
Gabry (2017), Vehtari, Simpson, Gelman, Yao, and Gabry (2024), and
Yao et al. (2018). To get started see the loo package
vignettes, the
loo() function for efficient approximate leave-one-out
cross-validation (LOO-CV), the psis() function for the Pareto
smoothed importance sampling (PSIS) algorithm, or
loo_model_weights() for an implementation of Bayesian stacking of
predictive distributions from multiple models.
Arguments
Author
Maintainer: Jonah Gabry jsg2201@columbia.edu
Authors:
Aki Vehtari Aki.Vehtari@aalto.fi
Måns Magnusson
Yuling Yao
Paul-Christian Bürkner
Topi Paananen
Andrew Gelman
Other contributors:
Ben Goodrich [contributor]
Juho Piironen [contributor]
Bruno Nicenboim [contributor]
Leevi Lindgren [contributor]
Details
Leave-one-out cross-validation (LOO-CV) 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-CV and WAIC have various advantages over simpler estimates of predictive error such as AIC and DIC but are less used in practice because they involve additional computational steps. This package implements the fast and stable computations for approximate LOO-CV laid out in Vehtari, Gelman, and Gabry (2017). From existing posterior simulation draws, we compute LOO-CV using Pareto smoothed importance sampling (PSIS; Vehtari, Simpson, Gelman, Yao, and Gabry, 2024), a new procedure for stabilizing and diagnosing importance weights. As a byproduct of our calculations, we also obtain approximate standard errors for estimated predictive errors and for comparing of predictive errors between two models.
We recommend PSIS-LOO-CV instead of WAIC, because PSIS provides useful diagnostics and effective sample size and Monte Carlo standard error estimates.
References
Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. 27(5), 1413--1432. doi:10.1007/s11222-016-9696-4 (journal version, preprint arXiv:1507.04544).
Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024). Pareto smoothed importance sampling. Journal of Machine Learning Research, 25(72):1-58. PDF
Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2018) Using stacking to average Bayesian predictive distributions. Bayesian Analysis, advance publication, doi:10.1214/17-BA1091. (online).
Magnusson, M., Riis Andersen, M., Jonasson, J. and Vehtari, A. (2019). Leave-One-Out Cross-Validation for Large Data. In Thirty-sixth International Conference on Machine Learning, PMLR 97:4244-4253.
Magnusson, M., Riis Andersen, M., Jonasson, J. and Vehtari, A. (2020). Leave-One-Out Cross-Validation for Model Comparison in Large Data. In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 108:341-351.
Epifani, I., MacEachern, S. N., and Peruggia, M. (2008). Case-deletion importance sampling estimators: Central limit theorems and related results. Electronic Journal of Statistics 2, 774-806.
Gelfand, A. E. (1996). Model determination using sampling-based methods. In Markov Chain Monte Carlo in Practice, ed. W. R. Gilks, S. Richardson, D. J. Spiegelhalter, 145-162. London: Chapman and Hall.
Gelfand, A. E., Dey, D. K., and Chang, H. (1992). Model determination using predictive distributions with implementation via sampling-based methods. In Bayesian Statistics 4, ed. J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, 147-167. Oxford University Press.
Gelman, A., Hwang, J., and Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and Computing 24, 997-1016.
Ionides, E. L. (2008). Truncated importance sampling. Journal of Computational and Graphical Statistics 17, 295-311.
Koopman, S. J., Shephard, N., and Creal, D. (2009). Testing the assumptions behind importance sampling. Journal of Econometrics 149, 2-11.
Peruggia, M. (1997). On the variability of case-deletion importance sampling weights in the Bayesian linear model. Journal of the American Statistical Association 92, 199-207.
Stan Development Team (2017). The Stan C++ Library, Version 2.17.0. https://mc-stan.org.
Stan Development Team (2018). RStan: the R interface to Stan, Version 2.17.3. https://mc-stan.org.
Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely application information criterion in singular learning theory. Journal of Machine Learning Research 11, 3571-3594.
Zhang, J., and Stephens, M. A. (2009). A new and efficient estimation method for the generalized Pareto distribution. Technometrics 51, 316-325.
See Also
Useful links: