prior

Uses the covariance of the prior distributions to define the MCMC proposal covariance.

Topics

bayesian_calibration

Specification

  • Alias: None

  • Arguments: None

Child Keywords:

Required/Optional

Description of Group

Dakota Keyword

Dakota Keyword Description

Optional

multiplier

Multiplier to scale prior variance

Description

This keyword selection results in definition of the MCMC proposal covariance from the covariance of the prior distributions. This covariance is currently assumed to be diagonal without correlation.

Default Behavior

This is the default proposal_covariance option.

Usage Tips

Since this proposal covariance is defined globally, the chain does not need to be periodically restarted using local updates to this proposal. However, it is usually effective to adapt the proposal using one of the adaptive metropolis MCMC options.

Examples

method,
        bayes_calibration queso
          samples = 2000 seed = 348
          dram
          proposal_covariance prior