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 accessAbstract
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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Citations
Cites
(2015) · doi:10.1007/s11222-016-9696-4 · Cited by 5,611
Self-citationUsing Stacking to Average Bayesian Predictive Distributions (with Discussion)
Yao, Y., Vehtari, A., Simpson, D., & Gelman, A. (2018) · Bayesian Analysis · doi:10.1214/17-ba1091 · Cited by 493
Self-citationLocal scale invariance and robustness of proper scoring rules
Bolin, D., & Wallin, J. (2022) · Statistical Science · doi:10.1214/22-sts864 · Cited by 36
Cited by
brms : An R Package for Bayesian Multilevel Models Using Stan
Bürkner, P.-C. (2017) · Journal of Statistical Software · doi:10.18637/jss.v080.i01 · Cited by 9,476
Self-citation(2015) · doi:10.1007/s11222-016-9696-4 · Cited by 5,611
Self-citationVisualization in Bayesian Workflow
Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., & Gelman, A. (2019) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/rssa.12378 · Cited by 1,122
Self-citationHumans incorporate attention-dependent uncertainty into perceptual decisions and confidence
Denison, R. N., Adler, W. T., Carrasco, M., & Ma, W. J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717720115 · Cited by 122
Acerbi, L., Dokka, K., Angelaki, D. E., & Ma, W. J. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006110 · Cited by 111
Metadata
- Authors
- Vehtari, A., Gabry, J., Magnusson, M., Yao, Y., Bürkner, P.-C., Paananen, T., & Gelman, A.
- Venue
- CRAN: Contributed Packages
- Publisher
- The R Foundation
- Published
- 2015-06-26
- OpenAlex
- W4399648958
- Cited by
- 357
- References
- 3
- Type
- dataset
- Language
- en
- Open access
- gold
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- Vehtari, A., Gabry, J., Magnusson, M., Yao, Y., Bürkner, P.-C., Paananen, T., & Gelman, A. (2015). loo: Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models. CRAN: Contributed Packages. https://doi.org/10.32614/cran.package.loo
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