Compute Shannon's Conditional-Entropy based on the chain rule \(H(X | Y)
= H(X,Y) - H(Y)\) based on a given joint-probability vector \(P(X,Y)\) and
probability vector \(P(Y)\).

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

CE function

<dl><dt>xy</dt>
<dd>a numeric joint-probability vector \(P(X,Y)\)
for which Shannon's Joint-Entropy \(H(X,Y)\) shall be computed.</dd>
<dt>y</dt>
<dd>a numeric probability vector \(P(Y)\) for which
Shannon's Entropy \(H(Y)\) (as part of the chain rule) shall be computed.
It is important to note that this probability vector must be the probability
distribution of random variable Y ( P(Y) for which H(Y) is computed).</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 Conditional-Entropy \(H(X | Y)\) — CE

<dl>

<dt>xy</dt>
<dd>a numeric joint-probability vector \(P(X,Y)\)
for which Shannon's Joint-Entropy \(H(X,Y)\) shall be computed.</dd>


<dt>y</dt>
<dd>a numeric probability vector \(P(Y)\) for which
Shannon's Entropy \(H(Y)\) (as part of the chain rule) shall be computed.
It is important to note that this probability vector must be the probability
distribution of random variable Y ( P(Y) for which H(Y) is computed).</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>

CE: Shannon's Conditional-Entropy \(H(X | Y)\)

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples