.. _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-export_chain_points_file
   method-bayes_calibration-muq-adaptive_metropolis
   method-bayes_calibration-muq-delayed_rejection
   method-bayes_calibration-muq-dili
   method-bayes_calibration-muq-dram
   method-bayes_calibration-muq-mala
   method-bayes_calibration-muq-metropolis_hastings
   method-bayes_calibration-muq-pre_solve
   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                                     | `export_chain_points_file`__ | Export the MCMC chain to the specified        |
|                                              |                              | filename                                      |
+-------------------------+--------------------+------------------------------+-----------------------------------------------+
| Optional (Choose One)   | MCMC Algorithm     | `adaptive_metropolis`__      | Use the Adaptive Metropolis MCMC algorithm    |
|                         |                    +------------------------------+-----------------------------------------------+
|                         |                    | `delayed_rejection`__        | Use the Delayed Rejection MCMC algorithm      |
|                         |                    +------------------------------+-----------------------------------------------+
|                         |                    | `dili`__                     | Dimension-independent likelihood-informed     |
|                         |                    |                              | MCMC                                          |
|                         |                    +------------------------------+-----------------------------------------------+
|                         |                    | `dram`__                     | Use the DRAM MCMC algorithm                   |
|                         |                    +------------------------------+-----------------------------------------------+
|                         |                    | `mala`__                     | Metropolis-adjusted Langevin algorithm        |
|                         |                    +------------------------------+-----------------------------------------------+
|                         |                    | `metropolis_hastings`__      | Use the Metropolis-Hastings MCMC algorithm    |
+-------------------------+--------------------+------------------------------+-----------------------------------------------+
| Optional                                     | `pre_solve`__                | Perform deterministic optimization for MAP    |
|                                              |                              | before Bayesian calibration                   |
+----------------------------------------------+------------------------------+-----------------------------------------------+
| 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-export_chain_points_file.html
__ method-bayes_calibration-muq-adaptive_metropolis.html
__ method-bayes_calibration-muq-delayed_rejection.html
__ method-bayes_calibration-muq-dili.html
__ method-bayes_calibration-muq-dram.html
__ method-bayes_calibration-muq-mala.html
__ method-bayes_calibration-muq-metropolis_hastings.html
__ method-bayes_calibration-muq-pre_solve.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), delayed-rejection adaptive metropolis (DRAM),
Metropolis-adjusted Langevin algorithm (MALA), or dimension-independent
likelihood-informed (DILI).

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/