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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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Compute diagnostic metrics for Markov chain
Alias: none
Argument(s): none
Child Keywords:
Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
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Optional | confidence_intervals | Calculate the confidence intervals on estimates of first and second moments |
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.