Diagnostics for Laplace and ADVI approximations and Laplace-loo and ADVI-loo
psis_approximate_posterior(
log_p = NULL,
log_g = NULL,
log_liks = NULL,
cores,
save_psis,
...,
log_q = NULL
)If log likelihoods are supplied, the function returns a "loo" object,
otherwise the function returns a "psis" object.
The log-posterior (target) evaluated at S samples from the proposal distribution (g). A vector of length S.
The log-density (proposal) evaluated at S samples from the proposal distribution (g). A vector of length S.
A log-likelihood matrix of size S * N, where N is the number
of observations and S is the number of samples from q. See
loo.matrix() for details. Default is NULL. Then only the
posterior is evaluated using the k_hat diagnostic.
The number of cores to use for parallelization. This defaults to
the option mc.cores which can be set for an entire R session by
options(mc.cores = NUMBER). The old option loo.cores is now
deprecated but will be given precedence over mc.cores until
loo.cores is removed in a future release. As of version
2.0.0 the default is now 1 core if mc.cores is not set, but we
recommend using as many (or close to as many) cores as possible.
Note for Windows 10 users: it is strongly
recommended to avoid using
the .Rprofile file to set mc.cores (using the cores argument or
setting mc.cores interactively or in a script is fine).
Should the psis object created internally by loo() be
saved in the returned object? The loo() function calls psis()
internally but by default discards the (potentially large) psis object
after using it to compute the LOO-CV summaries. Setting save_psis=TRUE
will add a psis_object component to the list returned by loo.
This is useful if you plan to use the E_loo() function to compute
weighted expectations after running loo. Several functions in the
bayesplot package also accept psis objects.
Deprecated argument name (the same as log_g).
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
loo() and psis()