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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. Landauer's principle - Wikipedia

    en.wikipedia.org/wiki/Landauer's_principle

    Landauer's principle is a physical principle pertaining to a lower theoretical limit of energy consumption of computation.It holds that an irreversible change in information stored in a computer, such as merging two computational paths, dissipates a minimum amount of heat to its surroundings. [1]

  4. Entropy in thermodynamics and information theory - Wikipedia

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

    Despite the foregoing, there is a difference between the two quantities. The information entropy Η can be calculated for any probability distribution (if the "message" is taken to be that the event i which had probability p i occurred, out of the space of the events possible), while the thermodynamic entropy S refers to thermodynamic probabilities p i specifically.

  5. 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 ...

  6. Entropic uncertainty - Wikipedia

    en.wikipedia.org/wiki/Entropic_uncertainty

    Hirschman [1] explained that entropy—his version of entropy was the negative of Shannon's—is a "measure of the concentration of [a probability distribution] in a set of small measure." Thus a low or large negative Shannon entropy means that a considerable mass of the probability distribution is confined to a set of small measure.

  7. Uncertainty coefficient - Wikipedia

    en.wikipedia.org/wiki/Uncertainty_coefficient

    In statistics, the uncertainty coefficient, also called proficiency, entropy coefficient or Theil's U, is a measure of nominal association. It was first introduced by Henri Theil [ citation needed ] and is based on the concept of information entropy .

  8. 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 ...

  9. Extremal principles in non-equilibrium thermodynamics

    en.wikipedia.org/wiki/Extremal_principles_in_non...

    Grandy (2008) [8] in section 4.3 on page 55 clarifies the distinction between the idea that entropy is related to order (which he considers to be an "unfortunate" "mischaracterization" that needs "debunking"), and the aforementioned idea of Jaynes that entropy is a measure of experimental reproducibility of process (which Grandy regards as ...