Blurb::
The number of samples used in Monte Carlo approximation of the model evidence.
Description::
The ``mc_approx`` keyword for model evidence indicates that
sample values will be generated from the prior distribution,
and then the simulation model will be evaluated at these
sample values to obtain corresponding likelihood values.
The average of the likelihood weighted by the prior is the
model evidence. The accuracy of this approximation depends on the
number of samples taken, which is specified by
the ``evidence_samples`` keyword.
Note that each sample specified by the ``evidence_samples`` keyword
will require an evaluation of the simulation model to compute the
corresponding likelihood. So, this may become costly for expensive
simulations.  Additionally, many prior samples will have very low
(near zero) likelihood, so millions of samples may be required for
accurate computation of the integral which defines model evidence.

*Default Behavior*

If ``evidence_samples`` is not specified with ``mc_approx``, Dakota
uses the number of chain samples from the MCMC ( ``chain_samples``)
as the number of samples to use for calculating the model evidence.

*Expected Output*
Currently, the model evidence will be printed in the screen output
with prefacing text indicating if it is calculated by
Monte Carlo sampling.

*Usage Tips*
Topics::

Examples::

Theory::

Faq::

See_Also::
