Computes log marginal likelihood via bridge sampling,
which can be used in the computation of bayes factors
and posterior model probabilities.
brmsfit method is just a thin wrapper around
the corresponding method for
Additional arguments passed to
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(all = TRUE) in the call to
brm, if you are planning to
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
multiple times to check the stability of the results.
More details are provided under
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## Not run: # 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) ## End(Not run)
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