Dakota Reference Manual: chain_diagnostics

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

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chain_diagnostics

Compute diagnostic metrics for Markov chain

## Specification

Alias: none

Argument(s): none

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Optional | confidence_intervals | Calculate the confidence intervals on estimates of first and second moments

## Description

While a Markov chain produced via Monte Carlo sampling eventually converges to a set of samples representative of an underlying probability distribution, the first set of samples may not accurately capture the target distribution. Chain diagnostic metrics provide measures of this convergence, and are intended to help users decide whether `samples` should be increased in Bayesian calibration exercises.