.. _method-bayes_calibration-muq:

"""
muq
"""


Markov Chain Monte Carlo algorithms from the MUQ package



**Topics**


bayesian_calibration


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

   method-bayes_calibration-muq-chain_samples
   method-bayes_calibration-muq-seed
   method-bayes_calibration-muq-rng
   method-bayes_calibration-muq-dram
   method-bayes_calibration-muq-delayed_rejection
   method-bayes_calibration-muq-adaptive_metropolis
   method-bayes_calibration-muq-metropolis_hastings
   method-bayes_calibration-muq-proposal_covariance


**Specification**

- *Alias:* None

- *Arguments:* None


**Child Keywords:**

+-------------------------+--------------------+-------------------------+---------------------------------------------+
| Required/Optional       | Description of     | Dakota Keyword          | Dakota Keyword Description                  |
|                         | Group              |                         |                                             |
+=========================+====================+=========================+=============================================+
| Required                                     | `chain_samples`__       | Number of Markov Chain Monte Carlo          |
|                                              |                         | posterior samples                           |
+----------------------------------------------+-------------------------+---------------------------------------------+
| Optional                                     | `seed`__                | Seed of the random number generator         |
+----------------------------------------------+-------------------------+---------------------------------------------+
| Optional                                     | `rng`__                 | Selection of a random number generator      |
+-------------------------+--------------------+-------------------------+---------------------------------------------+
| Optional (Choose One)   | MCMC Algorithm     | `dram`__                | Use the DRAM MCMC algorithm                 |
|                         |                    +-------------------------+---------------------------------------------+
|                         |                    | `delayed_rejection`__   | Use the Delayed Rejection MCMC algorithm    |
|                         |                    +-------------------------+---------------------------------------------+
|                         |                    | `adaptive_metropolis`__ | Use the Adaptive Metropolis MCMC algorithm  |
|                         |                    +-------------------------+---------------------------------------------+
|                         |                    | `metropolis_hastings`__ | Use the Metropolis-Hastings MCMC algorithm  |
+-------------------------+--------------------+-------------------------+---------------------------------------------+
| Optional                                     | `proposal_covariance`__ | Defines the technique used to generate the  |
|                                              |                         | MCMC proposal covariance.                   |
+----------------------------------------------+-------------------------+---------------------------------------------+

.. __: method-bayes_calibration-muq-chain_samples.html
__ method-bayes_calibration-muq-seed.html
__ method-bayes_calibration-muq-rng.html
__ method-bayes_calibration-muq-dram.html
__ method-bayes_calibration-muq-delayed_rejection.html
__ method-bayes_calibration-muq-adaptive_metropolis.html
__ method-bayes_calibration-muq-metropolis_hastings.html
__ method-bayes_calibration-muq-proposal_covariance.html



**Description**


The ``muq`` method supports the following MCMC algorithms:
adaptive metropolis (AM), Metropolis Hasting (MH), delayed
rejection (DR), or delayed-rejection adaptive metropolis (DRAM).

The ``muq`` method is currently an experimental method that relies
on algorithms from MIT's MUQ code documented at:
https://bitbucket.org/mituq/muq2/src/master/

We anticipate using more advanced features of MUQ such as Hamiltonian
Monte Carlo and Langevin methods in future releases of Dakota.