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Information entropy of a Bernoulli trial X. If X can assume values 0 and 1, entropy of X is defined as H(X) = -Pr(X=0) log₂ Pr(X=0) - Pr(X=1) log₂ Pr(X=1). It has value if Pr(X=0)=1 or Pr(X=1)=1. The entropy reaches maximum when Pr(X=0)=Pr(X=1)=1/2 (the value of entropy is then 1).

Copyright Brona. Original LaTeX/pstricks sources stored at User:Brona/Images/binary_entropy_plot.tex.

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• (del) (cur) 03:11, 1 August 2004 . . en:User:Brona Brona ( en:User_talk:Brona Talk) . . 558x522 (38967 bytes) (Information entropy of a Bernoulli random variable )