Posterior Model Probabilities from Marginal Likelihoods

Compute posterior model probabilities from marginal likelihoods. The brmsfit method is just a thin wrapper around the corresponding method for bridge objects.

# S3 method for brmsfit
post_prob(x, ..., prior_prob = NULL, model_names = NULL)

Arguments

x

A brmsfit object.

...

More brmsfit objects or further arguments passed to the underlying post-processing functions. In particular, see prepare_predictions for further supported arguments.

prior_prob

Numeric vector with prior model probabilities. If omitted, a uniform prior is used (i.e., all models are equally likely a priori). The default NULL corresponds to equal prior model weights.

model_names

If NULL (the default) will use model names derived from deparsing the call. Otherwise will use the passed values as model names.

Details

Computing the marginal likelihood requires samples of all variables defined in Stan's parameters block to be saved. Otherwise post_prob cannot be computed. Thus, please set save_all_pars = TRUE in the call to brm, if you are planning to apply post_prob to your models.

The computation of model probabilities based on bridge sampling requires a lot more posterior samples than usual. A good conservative rule of thump is perhaps 10-fold more samples (read: the default of 4000 samples may not be enough in many cases). If not enough posterior samples are provided, the bridge sampling algorithm tends to be unstable leading to considerably different results each time it is run. We thus recommend running post_prob multiple times to check the stability of the results.

More details are provided under bridgesampling::post_prob.

See also

bridge_sampler, bayes_factor

Examples

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_all_pars = TRUE
)
summary(fit1)

# model without the treatent effect
fit2 <- brm(
  count ~ zAge + zBase,
  data = epilepsy, family = negbinomial(),
  prior = prior(normal(0, 1), class = b),
  save_all_pars = TRUE
)
summary(fit2)

# compute the posterior model probabilities
post_prob(fit1, fit2)

# specify prior model probabilities
post_prob(fit1, fit2, prior_prob = c(0.8, 0.2))
}