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.