Dakota Reference Manual: ksg2

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ksg2

Use second Kraskov algorithm to compute mutual information

## Specification

Alias: none

Argument(s): none

## Description

This algorithm is derived in[58] . The mutual information between random variables is approximated by

where is the digamma function, is the number of nearest neighbors being used, and is the number of samples available for the joint distribution of the random variables. For each point in the joint distribution, and its nearest neighbors are projected into each marginal subpsace. For each subspace , is defined as the radius of the -ball containing all points. Then, is the number of points in the -th subspace within a distance of from the point . The angular brackets denote that the average of is taken over all points .

## Examples

    method
        bayes_calibration queso
          dram
          seed = 34785
          chain_samples = 1000
          posterior_stats mutual_info
            ksg2

    method
        bayes_calibration
          queso
          dram
          chain_samples = 1000 seed = 348
        experimental_design
          initial_samples = 5
          num_candidates = 10
          max_hifi_evaluations = 3
          ksg2