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  2. Counting sort - Wikipedia

    en.wikipedia.org/wiki/Counting_sort

    The initialization of the count array, and the second for loop which performs a prefix sum on the count array, each iterate at most k + 1 times and therefore take O(k) time. The other two for loops, and the initialization of the output array, each take O ( n ) time.

  3. Monte Carlo method - Wikipedia

    en.wikipedia.org/wiki/Monte_Carlo_method

    Count the number of points inside the quadrant, i.e. having a distance from the origin of less than 1. The ratio of the inside-count and the total-sample-count is an estimate of the ratio of the two areas, ⁠ π / 4 ⁠. Multiply the result by 4 to estimate π. In this procedure, the domain of inputs is the square that circumscribes the quadrant.

  4. Index of coincidence - Wikipedia

    en.wikipedia.org/wiki/Index_of_coincidence

    The products n(n − 1) count the number of combinations of n elements taken two at a time. (Actually this counts each pair twice; the extra factors of 2 occur in both numerator and denominator of the formula and thus cancel out.) Each of the n i occurrences of the i-th letter matches each of the remaining n i − 1 occurrences of the same letter.

  5. Reed–Solomon error correction - Wikipedia

    en.wikipedia.org/wiki/Reed–Solomon_error...

    Since the function () is also a polynomial of the same degree, this function gives rise to a codeword ((), …, ()); since = holds, this codeword is the cyclic left-shift of the original codeword derived from .

  6. Lyapunov exponent - Wikipedia

    en.wikipedia.org/wiki/Lyapunov_exponent

    In mathematics, the Lyapunov exponent or Lyapunov characteristic exponent of a dynamical system is a quantity that characterizes the rate of separation of infinitesimally close trajectories.

  7. Automatic differentiation - Wikipedia

    en.wikipedia.org/wiki/Automatic_differentiation

    In contrast, reverse accumulation requires the evaluated partial functions for the partial derivatives. Reverse accumulation therefore evaluates the function first and calculates the derivatives with respect to all independent variables in an additional pass. Which of these two types should be used depends on the sweep count.

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  9. Decision tree learning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_learning

    Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning.In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations.