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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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Markov Chain Monte Carlo algorithms from the MUQ package
This keyword is related to the topics:
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. |
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.