Uses of Package
elki.distance.probabilistic

Packages that use elki.distance.probabilistic

Package	Description
elki.distance.probabilistic	Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence, F-divergence, χ²-divergence, etc.

Classes in elki.distance.probabilistic used by elki.distance.probabilistic

Class	Description
ChiDistance	χ distance function, symmetric version.
ChiSquaredDistance	χ² distance function, symmetric version.
FisherRaoDistance	Fisher-Rao riemannian metric for (discrete) probability distributions.
HellingerDistance	Hellinger metric / affinity / kernel, Bhattacharyya coefficient, fidelity similarity, Matusita distance, Hellinger-Kakutani metric on a probability distribution.
JeffreyDivergenceDistance	Jeffrey Divergence for NumberVectors is a symmetric, smoothened version of the KullbackLeiblerDivergenceAsymmetricDistance.
JensenShannonDivergenceDistance	Jensen-Shannon Divergence for NumberVectors is a symmetric, smoothened version of the KullbackLeiblerDivergenceAsymmetricDistance.
KullbackLeiblerDivergenceAsymmetricDistance	Kullback-Leibler divergence, also known as relative entropy, information deviation, or just KL-distance (albeit asymmetric).
KullbackLeiblerDivergenceReverseAsymmetricDistance	Kullback-Leibler divergence, also known as relative entropy, information deviation or just KL-distance (albeit asymmetric).
SqrtJensenShannonDivergenceDistance	The square root of Jensen-Shannon divergence is a metric.
TriangularDiscriminationDistance	Triangular Discrimination has relatively tight upper and lower bounds to the Jensen-Shannon divergence, but is much less expensive.
TriangularDistance	Triangular Distance has relatively tight upper and lower bounds to the (square root of the) Jensen-Shannon divergence, but is much less expensive.