muq

Markov Chain Monte Carlo algorithms from the MUQ package

Topics

bayesian_calibration

Specification

  • Alias: None

  • Arguments: 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

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.