The law asserts that for two given macroscopically specified states of a system, there is a quantity called the difference of information entropy between them.
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In practice, information entropy is almost always calculated using base 2 logarithms, but this distinction amounts to nothing other than a change in units.
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Shannon developed information entropy as a measure for the uncertainty in a message while essentially inventing what became known as the dominant form of information theory.
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Using a statistical description for data, information theory quantifies the number of bits needed to describe the data, which is the information entropy of the source.
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In information theoretic terms, the information entropy of a system is the amount of " missing " information needed to determine a microstate, given the macrostate.
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Therefore, the information entropy must also remain constant, if we condition on the original information, and then follow each of those microstates forward in time:
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Indices of qualitative variation are then analogous to information entropy, which is minimized when all cases belong to a single category and maximized in a uniform distribution.
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The purpose of these corruptions is effectively to lower the information entropy of the data, enabling the data structure to break through information-theoretic barriers regarding heaps.
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It also developed the concepts of information entropy and redundancy, and introduced the term bit ( which Shannon credited to John Tukey ) as a unit of information.
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This is equivalent to Information Entropy divided by the \ log _ 2 ( K ) and is useful for comparing relative variation between frequency tables of multiple sizes.
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