![]() |
Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
|
Compute information-theoretic metrics on posterior parameter distribution
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 |
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.