Blurb::  Use the Metropolis-Hastings MCMC algorithm

Description::  
This keyword specifies the use of a Metropolis-Hastings algorithm for
the MCMC chain generation.  This means there is no delayed rejection
and no adaptive proposal covariance updating as in DRAM.

<b> Default Behavior </b>

Five MCMC algorithm variants are supported currently in QUESO: \c dram, \c
delayed_rejection, \c adaptive_metropolis, \c metropolis_hastings, and
\c multilevel.  The default is \c dram.

Four MCMC algorithm variants are currently supported in MUQ: \c dram, \c
delayed_rejection, \c metropolis_hastings and \c adaptive_metropolis.
 
<b> Usage Tips </b>

If the user wants to use Metropolis-Hastings, possibly as a comparison
to the other methods which involve more chain adaptation, this is the
MCMC type to use.


Topics::	bayesian_calibration	
Examples::
Examples::
\verbatim
method,
        bayes_calibration queso
          metropolis_hastings 
          samples = 10000 seed = 348
\endverbatim

Theory::
Faq::
See_Also::	
