Dakota Reference Manual: metropolis_hastings

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metropolis_hastings

Use the Metropolis-Hastings MCMC algorithm

## Topics

This keyword is related to the topics:

- bayesian_calibration

## Specification

Alias: none

Argument(s): none

Default: dram

## Description

This keyword specifies the use of a Metropolis-Hastings algorithm for the MCMC chain generation. This means there is no delayed rejection and no adaptive proposal covariance updating as in DRAM.

Default Behavior

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

Four MCMC algorithm variants are currently supported in MUQ: `dram`, `delayed_rejection`, `metropolis_hastings` and `adaptive_metropolis`.

Usage Tips

If the user wants to use Metropolis-Hastings, possibly as a comparison to the other methods which involve more chain adaptation, this is the MCMC type to use.

## Examples

    method,
            bayes_calibration queso
              metropolis_hastings
              samples = 10000 seed = 348