Dakota Reference Manual  Version 6.12
Explore and Predict with Confidence
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bayesian_calibration

Description

See the discussion of Bayesian Calibration in the Dakota User's Manual [5].

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
  • metropolis_hastings : Use the Metropolis-Hastings MCMC algorithm
  • 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.