Dakota Reference Manual: bayesian_calibration

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

- Main Page
- Related Pages

All Pages
bayesian_calibration

## Description

See the discussion of Bayesian Calibration in the Dakota User's Manual [4].

## Related Topics

## Related Keywords

- bayes_calibration : Bayesian calibration
- dream : DREAM (DiffeRential Evolution Adaptive Metropolis)
- chains : Number of chains in DREAM
- crossover_chain_pairs : Number of chains used in crossover.
- gr_threshold : Convergence tolerance for the Gelman-Rubin statistic
- jump_step : Number of generations a long jump step is taken
- num_cr : Number of candidate points for each crossover.
- gpmsa : (Experimental) Gaussian Process Models for Simulation Analysis (GPMSA) Bayesian calibration
- adaptive_metropolis : Use the Adaptive Metropolis MCMC algorithm
- delayed_rejection : Use the Delayed Rejection MCMC algorithm
- dram : Use the DRAM MCMC algorithm
- metropolis_hastings : Use the Metropolis-Hastings MCMC algorithm
- proposal_covariance : Defines the technique used to generate the MCMC proposal covariance.
- derivatives : Use derivatives to inform the MCMC proposal covariance.
- prior : Uses the covariance of the prior distributions to define the MCMC proposal covariance.
- muq : Markov Chain Monte Carlo algorithms from the MUQ package
- adaptive_metropolis : Use the Adaptive Metropolis MCMC algorithm
- delayed_rejection : Use the Delayed Rejection MCMC algorithm
- dram : Use the DRAM MCMC algorithm
- metropolis_hastings : Use the Metropolis-Hastings MCMC algorithm
- proposal_covariance : Defines the technique used to generate the MCMC proposal covariance.
- derivatives : Use derivatives to inform the MCMC proposal covariance.
- prior : Uses the covariance of the prior distributions to define the MCMC proposal covariance.
- queso : Markov Chain Monte Carlo algorithms from the QUESO package
- adaptive_metropolis : Use the Adaptive Metropolis MCMC algorithm
- delayed_rejection : Use the Delayed Rejection MCMC algorithm
- dram : Use the DRAM MCMC algorithm
- metropolis_hastings : Use the Metropolis-Hastings MCMC algorithm
- multilevel : Use the multilevel MCMC algorithm.
- proposal_covariance : Defines the technique used to generate the MCMC proposal covariance.
- derivatives : Use derivatives to inform the MCMC proposal covariance.
- prior : Uses the covariance of the prior distributions to define the MCMC proposal covariance.