Dakota Reference Manual: multilevel

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

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multilevel

Use the multilevel MCMC algorithm.

## Topics

This keyword is related to the topics:

- bayesian_calibration

## Specification

Alias: none

Argument(s): none

Default: dram

## Description

Selects the multilevel algorithm described in[73].

Default Behavior

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

Usage Tips

The multilevel algorithm is a more experimental algorithm than the other MCMC approaches mentioned above. It works well in cases where the prior can be "evolved" to a posterior in a structured way. Currently, the multilevel option is not in production form.

## Examples

    method,
            bayes_calibration queso
              multilevel
              samples = 10000 seed = 348