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Transferred from en.wikipedia to Commons by Maksim.
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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date/time | username | edit summary |
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03:20, 1 August 2004 | en:User:Brona | (more info) |
03:11, 1 August 2004 | en:User:Brona | (Information entropy of a Bernoulli random variable ) |
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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 )
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current | 14:44, 19 March 2006 | ![]() | 558 × 522 (38 KB) | Maksim (talk | contribs) | La bildo estas kopiita de wikipedia:en. La originala priskribo estas: 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) log<sub>2</sub> Pr(''X''=0) - Pr( |
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