deletions | additions       diff --git a/subsection_information_theory_Information_theory__.tex b/subsection_information_theory_Information_theory__.tex  index b5f5ee1..f6be026 100644  --- a/subsection_information_theory_Information_theory__.tex  +++ b/subsection_information_theory_Information_theory__.tex 
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\[  JS(A;B) = \frac{1}{2} D(A||M) + \frac{1}{2} D(B||M)  \]  where $\phi(M_x) = \frac{1}{2}\phi(A_x) \frac{1}{2}\big(\phi(A_x)  + \frac{1}{2}\phi(B_x)$ \phi(B_x)\big)$  From its definition it can easily be shown that   \[  JS(A,B) = H(M) - \frac{1}{2}H(A) \frac{1}{2}(H(A)  - \frac{1}{2}H(B) H(B))  \]  That is, the Jensen-Shannon divergence is equal to the entropy of the average distribution of two distributions minus the average of the entropies of the individual distributions.