Blurb::
Calculate model evidence (marginal likelihood of model) when using Bayesian methods

Description::
Model evidence is used in Bayesian model selection and model averaging. 
It is defined as the probability of the data given the model, and 
is calculated by averaging the likelihood of the model parameters 
over all values of the model parameters according to their prior 
distributions. In Dakota, one must calculate the 
model evidence separately for each model and perform the normalization 
to obtain the posterior model plausibility for each model. 

<b> Default Behavior </b>

When specifying \c model_evidence, there are two methods of 
calculating it.  One or both may be specified.  They 
include the Monte Carlo approximation, given by \c mc_approx 
and the Laplace approximation, given by \c laplace_approx.  \c mc_approx 
is the default approach. 

<b> Expected Output </b>
Currently, the model evidence will be printed in the screen output 
with prefacing text indicating if it is calculated by 
Monte Carlo sampling or the Laplace approximation. 

<b> Usage Tips </b>


Topics::

Examples::

Theory::
Faq::
See_Also::
