enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Joint probability distribution - Wikipedia

    en.wikipedia.org/wiki/Joint_probability_distribution

    In probability theory, the joint probability distribution is the probability distribution of all possible pairs of outputs of two random variables that are defined on the same probability space. The joint distribution can just as well be considered for any given number of random variables.

  3. 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.

  4. 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]

  5. Likelihood function - Wikipedia

    en.wikipedia.org/wiki/Likelihood_function

    The fact that the likelihood function can be defined in a way that includes contributions that are not commensurate (the density and the probability mass) arises from the way in which the likelihood function is defined up to a constant of proportionality, where this "constant" can change with the observation , but not with the parameter .

  6. Multivariate kernel density estimation - Wikipedia

    en.wikipedia.org/wiki/Multivariate_kernel...

    The goal of density estimation is to take a finite sample of data and to make inferences about the underlying probability density function everywhere, including where no data are observed. In kernel density estimation, the contribution of each data point is smoothed out from a single point into a region of space surrounding it.

  7. Notation in probability and statistics - Wikipedia

    en.wikipedia.org/wiki/Notation_in_probability...

    The probability is sometimes written to distinguish it from other functions and measure P to avoid having to define "P is a probability" and () is short for ({: ()}), where is the event space, is a random variable that is a function of (i.e., it depends upon ), and is some outcome of interest within the domain specified by (say, a particular ...

  8. Chain rule (probability) - Wikipedia

    en.wikipedia.org/wiki/Chain_rule_(probability)

    This rule allows one to express a joint probability in terms of only conditional probabilities. [4] The rule is notably used in the context of discrete stochastic processes and in applications, e.g. the study of Bayesian networks, which describe a probability distribution in terms of conditional probabilities.

  9. 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)