INFORMATION & COMMUNICATION

discrete information entropy

measures the average uncertainty of each output of a random source.

H(X)=−∑xp(x)log⁡2p(x)H(X)=-\sum_x p(x)\log_2p(x)

symbols, variables and units

p(x): probability; H: bit; agreed to 0 log 0=0.

applicable conditions and boundaries

Discrete distribution; continuous differential entropy differs from this definition and can be negative.

formula source code

The following is a copyable LaTeX expression.

H(X)=-\sum_x p(x)\log_2p(x)

Reference and Extended Learning

MIT OpenCourseWare · Information Theory ↗

is organized according to model definition and assumptions. Please check actual conditions and original literature before engineering, research and clinical use.

entropyinformation entropy

How can this knowledge be incorporated into high-end products?

Relevant scientific figures and methodological contributions

Same subject formula

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