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  2. Mode (statistics) - Wikipedia

    en.wikipedia.org/wiki/Mode_(statistics)

    In statistics, the mode is the value that appears most often in a set of data values. [1] If X is a discrete random variable, the mode is the value x at which the probability mass function takes its maximum value (i.e., x=argmax x i P(X = x i)). In other words, it is the value that is most likely to be sampled.

  3. Modes of variation - Wikipedia

    en.wikipedia.org/wiki/Modes_of_variation

    In real-world applications, modes of variation associated with eigencomponents allow to interpret complex data, such as the evolution of function traits [5] and other infinite-dimensional data. [6] To illustrate how modes of variation work in practice, two examples are shown in the graphs to the right, which display the first two modes of ...

  4. Normal mode - Wikipedia

    en.wikipedia.org/wiki/Normal_mode

    A mode of vibration is characterized by a modal frequency and a mode shape. It is numbered according to the number of half waves in the vibration. For example, if a vibrating beam with both ends pinned displayed a mode shape of half of a sine wave (one peak on the vibrating beam) it would be vibrating in mode 1.

  5. Triangular distribution - Wikipedia

    en.wikipedia.org/wiki/Triangular_distribution

    This distribution for a = 0, b = 1 and c = 0.5—the mode (i.e., the peak) is exactly in the middle of the interval—corresponds to the distribution of the mean of two standard uniform variables, that is, the distribution of X = (X 1 + X 2) / 2, where X 1, X 2 are two independent random variables with standard uniform distribution in [0, 1]. [1]

  6. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    The moment generating function of a real random variable ⁠ ⁠ is the expected value of , as a function of the real parameter ⁠ ⁠. For a normal distribution with density ⁠ f {\displaystyle f} ⁠ , mean ⁠ μ {\displaystyle \mu } ⁠ and variance σ 2 {\textstyle \sigma ^{2}} , the moment generating function exists and is equal to

  7. Dynamic mode decomposition - Wikipedia

    en.wikipedia.org/wiki/Dynamic_mode_decomposition

    In data science, dynamic mode decomposition (DMD) is a dimensionality reduction algorithm developed by Peter J. Schmid and Joern Sesterhenn in 2008. [ 1 ] [ 2 ] Given a time series of data, DMD computes a set of modes, each of which is associated with a fixed oscillation frequency and decay/growth rate.

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  9. Unimodality - Wikipedia

    en.wikipedia.org/wiki/Unimodality

    The definition of "unimodal" was extended to functions of real numbers as well. A common definition is as follows: a function f(x) is a unimodal function if for some value m, it is monotonically increasing for x ≤ m and monotonically decreasing for x ≥ m. In that case, the maximum value of f(x) is f(m) and there are no other local maxima.