.. _method-bayes_calibration-muq-metropolis_hastings:

"""""""""""""""""""
metropolis_hastings
"""""""""""""""""""


Use the Metropolis-Hastings MCMC algorithm



**Topics**


bayesian_calibration


.. toctree::
   :hidden:
   :maxdepth: 1



**Specification**

- *Alias:* None

- *Arguments:* None

- *Default:* dram


**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.



**Examples**



.. code-block::

    method,
            bayes_calibration queso
              metropolis_hastings
              samples = 10000 seed = 348




