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  2. Probability mass function - Wikipedia

    en.wikipedia.org/wiki/Probability_mass_function

    The graph of a probability mass function. All the values of this function must be non-negative and sum up to 1. In probability and statistics, a probability mass function (sometimes called probability function or frequency function [1]) is a function that gives the probability that a discrete random variable is exactly equal to some value. [2]

  3. Kernel (statistics) - Wikipedia

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

    In statistics, especially in Bayesian statistics, the kernel of a probability density function (pdf) or probability mass function (pmf) is the form of the pdf or pmf in which any factors that are not functions of any of the variables in the domain are omitted. [1] Note that such factors may well be functions of the parameters of the

  4. Probability-generating function - Wikipedia

    en.wikipedia.org/wiki/Probability-generating...

    It is equivalent to, and sometimes called, the z-transform of the probability mass function. Other generating functions of random variables include the moment-generating function, the characteristic function and the cumulant generating function.

  5. Yule–Simon distribution - Wikipedia

    en.wikipedia.org/wiki/Yule–Simon_distribution

    In probability and statistics, the Yule–Simon distribution is a discrete probability distribution named after Udny Yule and Herbert A. Simon. Simon originally called it the Yule distribution. [1] The probability mass function (pmf) of the Yule–Simon (ρ) distribution is

  6. File:Form 1040 (2021).pdf - Wikipedia

    en.wikipedia.org/wiki/File:Form_1040_(2021).pdf

    Original file (1,275 × 1,650 pixels, file size: 173 KB, MIME type: application/pdf, 2 pages) This is a file from the Wikimedia Commons . Information from its description page there is shown below.

  7. Skellam distribution - Wikipedia

    en.wikipedia.org/wiki/Skellam_distribution

    The probability mass function of a Poisson-distributed random variable with mean μ is given by (;) =!.for (and zero otherwise). The Skellam probability mass function for the difference of two independent counts = is the convolution of two Poisson distributions: (Skellam, 1946)

  8. Logarithmic distribution - Wikipedia

    en.wikipedia.org/wiki/Logarithmic_distribution

    This leads directly to the probability mass function of a Log(p)-distributed random variable: = ⁡ for k ≥ 1, and where 0 < p < 1. Because of the identity above, the distribution is properly normalized. The cumulative distribution function is

  9. Probability density function - Wikipedia

    en.wikipedia.org/wiki/Probability_density_function

    The probability mass function of a discrete random variable is the density with respect to the counting measure over the sample space (usually the set of integers, or some subset thereof). It is not possible to define a density with reference to an arbitrary measure (e.g. one can not choose the counting measure as a reference for a continuous ...

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