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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. Probability-generating function - Wikipedia

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

    If X is a discrete random variable taking values x in the non-negative integers {0,1, ...}, then the probability generating function of X is defined as [1] = ⁡ = = (),where is the probability mass function of .

  4. 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

  5. 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

  6. Probability distribution - Wikipedia

    en.wikipedia.org/wiki/Probability_distribution

    Probability mass function (pmf): function that gives the probability that a discrete random variable is equal to some value. Frequency distribution : a table that displays the frequency of various outcomes in a sample .

  7. Convolution of probability distributions - Wikipedia

    en.wikipedia.org/wiki/Convolution_of_probability...

    The probability distribution of the sum of two or more independent random variables is the convolution of their individual distributions. The term is motivated by the fact that the probability mass function or probability density function of a sum of independent random variables is the convolution of their corresponding probability mass functions or probability density functions respectively.

  8. 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.

  9. Flory–Schulz distribution - Wikipedia

    en.wikipedia.org/wiki/Flory–Schulz_distribution

    The probability mass function (pmf) for the mass fraction of chains of length is: () = (). In this equation, k is the number of monomers in the chain, [ 1 ] and 0<a<1 is an empirically determined constant related to the fraction of unreacted monomer remaining.