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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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03:20, 1 August 2004 |
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03:11, 1 August 2004 |
en:User:Brona |
(Information entropy of a Bernoulli random variable ) |
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