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  2. Entropy (information theory) - Wikipedia

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

    If one considers the text of every book ever published as a sequence, with each symbol being the text of a complete book, and if there are N published books, and each book is only published once, the estimate of the probability of each book is 1/N, and the entropy (in bits) is −log 2 (1/N) = log 2 (N).

  3. 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.

  4. Information gain (decision tree) - Wikipedia

    en.wikipedia.org/wiki/Information_gain_(decision...

    The relatively high value of entropy () = (1 is the optimal value) suggests that the root node is highly impure and the constituents of the input at the root node would look like the leftmost figure in the above Entropy Diagram. However, such a set of data is good for learning the attributes of the mutations used to split the node.

  5. Mutual information - Wikipedia

    en.wikipedia.org/wiki/Mutual_information

    The joint information is equal to the mutual information plus the sum of all the marginal information (negative of the marginal entropies) for each particle coordinate. Boltzmann's assumption amounts to ignoring the mutual information in the calculation of entropy, which yields the thermodynamic entropy (divided by the Boltzmann constant).

  6. Entropy estimation - Wikipedia

    en.wikipedia.org/wiki/Entropy_estimation

    A new approach to the problem of entropy evaluation is to compare the expected entropy of a sample of random sequence with the calculated entropy of the sample. The method gives very accurate results, but it is limited to calculations of random sequences modeled as Markov chains of the first order with small values of bias and correlations ...

  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. Boltzmann's entropy formula - Wikipedia

    en.wikipedia.org/wiki/Boltzmann's_entropy_formula

    Boltzmann's equation—carved on his gravestone. [1]In statistical mechanics, Boltzmann's equation (also known as the Boltzmann–Planck equation) is a probability equation relating the entropy, also written as , of an ideal gas to the multiplicity (commonly denoted as or ), the number of real microstates corresponding to the gas's macrostate:

  9. Departure function - Wikipedia

    en.wikipedia.org/wiki/Departure_function

    The Peng–Robinson equation of state relates the three interdependent state properties pressure P, temperature T, and molar volume V m.From the state properties (P, V m, T), one may compute the departure function for enthalpy per mole (denoted h) and entropy per mole (s): [2]