Blurb::
Calculate model evidence using the Laplace approximation

Description::
The \c laplace_approx keyword for model evidence indicates that 
a pre-solve will be used prior to the Bayesian MCMC sampling 
to estimate the Maximum A Posteriori (MAP) point.  The Laplace 
approximation assumes the posterior density is nearly 
Gaussian and is given by a formula which involves the likelihood 
at the MAP point, the prior density at the MAP point, and the 
Hessian of the log-posterior at the MAP point.  The formula is 
given in the Dakota User's manual.  This method is efficient 
at estimating the model evidence for posterior densities with weak
non-Gaussian characteristics but it does require a MAP 
solve (so \c pre-solve should be specified) and it does 
require gradient and Hessians of the response to be on. 

<b> Default Behavior </b>


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

<b> Usage Tips </b>


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Theory::
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