Entropy is a term for several numerical measures of uncertainty, information, disorder, or dynamical complexity whose precise definition depends on context. In information
theory, the entropy of a discrete random variable
is its Shannon entropy. Related quantities include
conditional entropy, cross
entropy, and relative entropy, while Rényi
entropy, Tsallis entropy, min-entropy,
and max-entropy give generalizations or extremal
variants. The continuous analogue of Shannon entropy is differential
entropy.
In graph theory, graph entropy refers to several information-theoretic and structural quantities attached
to a graph.
Dynamical notions include Kolmogorov entropy and topological entropy (Ott 1993), while
von Neumann entropy is a matrix entropy used
in quantum information.
See also
Conditional Entropy,
Cross Entropy,
Differential Entropy,
Graph
Entropy,
Information Theory,
Joint
Entropy,
Kolmogorov Entropy,
Max-Entropy,
Maximum Entropy Method,
Metric
Entropy,
Min-Entropy,
Mutual
Information,
Nat,
Ornstein's
Theorem,
Redundancy,
Relative
Entropy,
Rényi Entropy,
Shannon
Entropy,
Topological Entropy,
Tsallis
Entropy,
von Neumann Entropy
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References
Lasota, A. and Mackey, M. C. Chaos, Fractals, and Noise: Stochastic Aspects of Dynamics, 2nd ed. New York: Springer-Verlag,
1994.Ott, E. "Entropies." §4.5 in Chaos
in Dynamical Systems. New York: Cambridge University Press, pp. 138-144,
1993.Referenced on Wolfram|Alpha
Entropy
Cite this as:
Weisstein, Eric W. "Entropy." From MathWorld--A Wolfram Resource. https://mathworld.wolfram.com/Entropy.html
Subject classifications
