Dakota Reference Manual  Version 6.12
Explore and Predict with Confidence
 All Pages

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


This keyword is related to the topics:


Alias: none

Argument(s): none

Child Keywords:

Required/Optional Description of Group Dakota Keyword Dakota Keyword Description
Optional multiplier

Multiplier to scale prior variance


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.


        bayes_calibration queso
          samples = 2000 seed = 348
          proposal_covariance prior