Dakota Reference Manual: prior

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

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prior

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

## Topics

This keyword is related to the topics:

- bayesian_calibration

## Specification

Alias: none

Argument(s): 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