Compute Shannon's Mutual Information based on the identity \(I(X,Y) =
H(X) + H(Y) - H(X,Y)\) based on a given joint-probability vector \(P(X,Y)\)
and probability vectors \(P(X)\) and \(P(Y)\).

Computes 46 optimized distance and similarity measures for comparing probability functions (Drost (2018) <doi:10.21105/joss.00765>). These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.

Hajk-Georg Drost

philentropy

Similarity and Distance Quantification Between Probability
Functions

Jakub Nowosad

MI function

<dl><dt>x</dt>
<dd>a numeric probability vector \(P(X)\).</dd>
<dt>y</dt>
<dd>a numeric probability vector \(P(Y)\).</dd>
<dt>xy</dt>
<dd>a numeric joint-probability vector \(P(X,Y)\).</dd>
<dt>unit</dt>
<dd>a character string specifying the logarithm unit that shall be used to compute distances that depend on log computations.</dd></dl>

Arguments

Author

Shannon's Mutual Information \(I(X,Y)\) — MI

<dl>

<dt>x</dt>
<dd>a numeric probability vector \(P(X)\).</dd>


<dt>y</dt>
<dd>a numeric probability vector \(P(Y)\).</dd>


<dt>xy</dt>
<dd>a numeric joint-probability vector \(P(X,Y)\).</dd>


<dt>unit</dt>
<dd>a character string specifying the logarithm unit that shall be used to compute distances that depend on log computations.</dd>

</dl>

MI: Shannon's Mutual Information \(I(X,Y)\)

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples