Description Usage Arguments Details Value References Examples
Compute the widely applicable information criterion (WAIC)
based on the posterior likelihood using the loo package.
For more details see waic
.
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x |
A varstan object |
... |
additional values need in waic methods |
See the loo_compare
function of the loo package
for more details on model comparisons.
An object of class loo
. With the estimates of the
Watanabe-Akaike Information criteria.
Vehtari, A., Gelman, A., & Gabry J. (2016). Practical Bayesian model
evaluation using leave-one-out cross-validation and WAIC. In Statistics
and Computing, doi:10.1007/s11222-016-9696-4
.
Gelman, A., Hwang, J., & Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and Computing. 24, 997-1016.
Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. The Journal of Machine Learning Research. 11, 3571-3594.
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