Dakota Reference Manual: muq

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muq

Markov Chain Monte Carlo algorithms from the MUQ package

## Topics

This keyword is related to the topics:

- bayesian_calibration

## Specification

Alias: none

Argument(s): none

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| 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 (Group 1) | 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.

## 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.