.. _method-bayes_calibration-model_evidence-laplace_approx:

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laplace_approx
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Calculate model evidence using the Laplace approximation


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**Specification**

- *Alias:* None

- *Arguments:* None


**Description**


The ``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 ``pre-solve`` should be specified) and it does
require gradient and Hessians of the response to be on.

*Default Behavior*


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

*Usage Tips*


