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  2. Entropy (information theory) - Wikipedia

    en.wikipedia.org/wiki/Entropy_(information_theory)

    The first 128 symbols of the Fibonacci sequence has an entropy of approximately 7 bits/symbol, but the sequence can be expressed using a formula [F(n) = F(n−1) + F(n−2) for n = 3, 4, 5, ..., F(1) =1, F(2) = 1] and this formula has a much lower entropy and applies to any length of the Fibonacci sequence.

  3. Orders of magnitude (entropy) - Wikipedia

    en.wikipedia.org/wiki/Orders_of_magnitude_(entropy)

    9.5699 × 10 −24 J⋅K −1: Entropy equivalent of one bit of information, equal to k times ln(2) [1] 10 −23: 1.381 × 10 −23 J⋅K −1: Boltzmann constant, entropy equivalent of one nat of information. 10 1: 5.74 J⋅K −1: Standard entropy of 1 mole of graphite [2] 10 33: ≈ 10 35 J⋅K −1: Entropy of the Sun (given as ≈ 10 42 ...

  4. Entropy in thermodynamics and information theory - Wikipedia

    en.wikipedia.org/wiki/Entropy_in_thermodynamics...

    Common values of b are 2, Euler's number e, and 10, and the unit of entropy is shannon (or bit) for b = 2, nat for b = e, and hartley for b = 10. [ 1 ] Mathematically H may also be seen as an average information, taken over the message space, because when a certain message occurs with probability p i , the information quantity −log( p i ...

  5. Entropy (order and disorder) - Wikipedia

    en.wikipedia.org/wiki/Entropy_(order_and_disorder)

    The relationship between entropy, order, and disorder in the Boltzmann equation is so clear among physicists that according to the views of thermodynamic ecologists Sven Jorgensen and Yuri Svirezhev, "it is obvious that entropy is a measure of order or, most likely, disorder in the system."

  6. Cross-entropy - Wikipedia

    en.wikipedia.org/wiki/Cross-entropy

    This is also known as the log loss (or logarithmic loss [4] or logistic loss); [5] the terms "log loss" and "cross-entropy loss" are used interchangeably. [ 6 ] More specifically, consider a binary regression model which can be used to classify observations into two possible classes (often simply labelled 0 {\displaystyle 0} and 1 ...

  7. Gibbs' inequality - Wikipedia

    en.wikipedia.org/wiki/Gibbs'_inequality

    [1]: 68 Put in words, the information entropy of a distribution is less than or equal to its cross entropy with any other distribution . The difference between the two quantities is the Kullback–Leibler divergence or relative entropy, so the inequality can also be written: [2]: 34

  8. Von Neumann entropy - Wikipedia

    en.wikipedia.org/wiki/Von_Neumann_entropy

    In physics, the von Neumann entropy, named after John von Neumann, is a measure of the statistical uncertainty within a description of a quantum system.It extends the concept of Gibbs entropy from classical statistical mechanics to quantum statistical mechanics, and it is the quantum counterpart of the Shannon entropy from classical information theory.

  9. Sackur–Tetrode equation - Wikipedia

    en.wikipedia.org/wiki/Sackur–Tetrode_equation

    The Sackur–Tetrode equation is an expression for the entropy of a monatomic ideal gas. [ 1 ] It is named for Hugo Martin Tetrode [ 2 ] (1895–1931) and Otto Sackur [ 3 ] (1880–1914), who developed it independently as a solution of Boltzmann's gas statistics and entropy equations, at about the same time in 1912.