Dakota Reference Manual: dram

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dram

Use the DRAM MCMC algorithm

## Topics

This keyword is related to the topics:

- bayesian_calibration

## Specification

Alias: none

Argument(s): none

Default: dram

## Description

The type of Markov Chain Monte Carlo used. This keyword specifies the use of DRAM, (Delayed Rejection Adaptive Metropolis)[39].

Default Behavior

Five MCMC algorithm variants are supported: `dram`, `delayed_rejection`, `adaptive_metropolis`, `metropolis_hastings`, and `multilevel`. The default is `dram`.

Usage Tips

If the user knows very little about the proposal covariance, using dram is a recommended strategy. The proposal covariance is adaptively updated, and the delayed rejection may help improve low acceptance rates.

## Examples

    method,
            bayes_calibration queso
              dram
              samples = 10000 seed = 348