Dakota Reference Manual: posterior_stats

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

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posterior_stats

Compute information-theoretic metrics on posterior parameter distribution

## Specification

Alias: none

Argument(s): none

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Optional | kl_divergence | Calculate the Kullback-Leibler Divergence between prior and posterior
| Optional | mutual_info | Calculate the mutual information between prior and posterior
| Optional | kde | Calculate the Kernel Density Estimate of the posterior distribution

## Description

Information theory allows for the calculation and quantification of the information contained within a distribution. It is particularly useful for distributions which are not Gaussian, such as those which are bimodal or highly skewed. The `posterior_stats` command calculates information metrics relating to the posterior distribution of the model parameters. These metrics are approximated by making use of the MCMC chain produced during the Bayesian update. This capability can be used with any Bayesian method.