This function approximates the model's marginal likelihood.
Usage
mml(x, S = 0, ncores = parallel::detectCores() - 1, recompute = FALSE)
# S3 method for class 'RprobitB_mml'
print(x, log = FALSE, ...)
# S3 method for class 'RprobitB_mml'
plot(x, log = FALSE, ...)Arguments
- x
An object of class
RprobitB_fit.- S
[
integer(1)]
The number of prior samples for the prior arithmetic mean estimate. Per default,S = 0. In this case, only the posterior samples are used for the approximation via the posterior harmonic mean estimator, see the details section.- ncores
[
integer(1)]
The number of cores for parallel computation.If set to 1, no parallel backend is used.
- recompute
[
logical(1)]
Recompute the probabilities?- log
[
logical(1)]
Return the logarithm of the marginal model likelihood?- ...
Currently not used.
Value
The object x, including the object mml, which is the model's
approximated marginal likelihood value.
Details
The model's marginal likelihood \(p(y\mid M)\) for a model \(M\) and data \(y\) is required for the computation of Bayes factors. In general, the term has no closed form and must be approximated numerically.
This function uses the posterior Gibbs samples to approximate the model's
marginal likelihood via the posterior harmonic mean estimator.
To check the convergence, call plot(x$mml), where x is the output
of this function. If the estimation does not seem to have
converged, you can improve the approximation by combining the value
with the prior arithmetic mean estimator. The final approximation of the
model's marginal likelihood than is a weighted sum of the posterior harmonic
mean estimate and the prior arithmetic mean estimate,
where the weights are determined by the sample sizes.