Computes log marginal likelihood via bridge sampling,
which can be used in the computation of bayes factors
and posterior model probabilities.
The brmsfit method is just a thin wrapper around
the corresponding method for stanfit objects.
# S3 method for brmsfit
bridge_sampler(samples, recompile = FALSE, ...)A brmsfit object.
Logical, indicating whether the Stan model should be recompiled. This may be necessary if you are running bridge sampling on another machine than the one used to fit the model. No recompilation is done by default.
Additional arguments passed to
bridge_sampler.stanfit.
Computing the marginal likelihood requires samples of all variables
defined in Stan's parameters block to be saved. Otherwise
bridge_sampler cannot be computed. Thus, please set save_pars
= save_pars(all = TRUE) in the call to brm, if you are planning to
apply bridge_sampler to your models.
The computation of marginal likelihoods based on bridge sampling requires
a lot more posterior draws than usual. A good conservative
rule of thump is perhaps 10-fold more draws (read: the default of 4000
draws may not be enough in many cases). If not enough posterior
draws are provided, the bridge sampling algorithm tends to be
unstable leading to considerably different results each time it is run.
We thus recommend running bridge_sampler
multiple times to check the stability of the results.
More details are provided under
bridgesampling::bridge_sampler.
if (FALSE) {
# model with the treatment effect
fit1 <- brm(
count ~ zAge + zBase + Trt,
data = epilepsy, family = negbinomial(),
prior = prior(normal(0, 1), class = b),
save_pars = save_pars(all = TRUE)
)
summary(fit1)
bridge_sampler(fit1)
# model without the treatment effect
fit2 <- brm(
count ~ zAge + zBase,
data = epilepsy, family = negbinomial(),
prior = prior(normal(0, 1), class = b),
save_pars = save_pars(all = TRUE)
)
summary(fit2)
bridge_sampler(fit2)
}