Jensen-Shannon Divergence JS-Divergence Equation Formula Latex Code and Summary

Jensen-Shannon Divergence JS-Divergence

Tags: #machine learning

                                 JS(P||Q)=\frac{1}{2}KL(P||\frac{(P+Q)}{2})+\frac{1}{2}KL(Q||\frac{(P+Q)}{2})

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$JS(P||Q)=\frac{1}{2}KL(P||\frac{(P+Q)}{2})+\frac{1}{2}KL(Q||\frac{(P+Q)}{2})$

            JS(P||Q)=\frac{1}{2}KL(P||\frac{(P+Q)}{2})+\frac{1}{2}KL(Q||\frac{(P+Q)}{2})

Latex code for the Jensen-Shannon Divergence(JS-Divergence). I will briefly introduce the notations in this formulation.

$KL(P||Q)$ : KL Divergence between P and Q
$JS(P||Q)$ : JS Divergence between P and Q, which is the symmetric divergence metric between distribution P and Q
$P(x)$ : Distribution of P(x) over x
$Q(x)$ : Distribution of Q(x) over x

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