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.

*Default Behavior*

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

Four MCMC algorithm variants are currently supported in MUQ: ``dram``,
``delayed_rejection``, ``metropolis_hastings`` and ``adaptive_metropolis``.

*Usage Tips*

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::

.. code-block::

    method,
            bayes_calibration queso
              metropolis_hastings
              samples = 10000 seed = 348


Theory::

Faq::

See_Also::
