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  2. Continuous uniform distribution - Wikipedia

    en.wikipedia.org/wiki/Continuous_uniform...

    In probability theory and statistics, the continuous uniform distributions or rectangular distributions are a family of symmetric probability distributions. Such a distribution describes an experiment where there is an arbitrary outcome that lies between certain bounds. [ 1 ]

  3. Independent and identically distributed random variables

    en.wikipedia.org/wiki/Independent_and...

    A chart showing a uniform distribution. In probability theory and statistics, a collection of random variables is independent and identically distributed (i.i.d., iid, or IID) if each random variable has the same probability distribution as the others and all are mutually independent. [1]

  4. Discrete uniform distribution - Wikipedia

    en.wikipedia.org/wiki/Discrete_uniform_distribution

    In probability theory and statistics, the discrete uniform distribution is a symmetric probability distribution wherein each of some finite whole number n of outcome values are equally likely to be observed. Thus every one of the n outcome values has equal probability 1/n. Intuitively, a discrete uniform distribution is "a known, finite number ...

  5. Irwin–Hall distribution - Wikipedia

    en.wikipedia.org/wiki/Irwin–Hall_distribution

    In probability and statistics, the Irwin–Hall distribution, named after Joseph Oscar Irwin and Philip Hall, is a probability distribution for a random variable defined as the sum of a number of independent random variables, each having a uniform distribution. [1] For this reason it is also known as the uniform sum distribution.

  6. Univariate distribution - Wikipedia

    en.wikipedia.org/wiki/Univariate_distribution

    Continuous uniform distribution. One of the simplest examples of a discrete univariate distribution is the discrete uniform distribution, where all elements of a finite set are equally likely. It is the probability model for the outcomes of tossing a fair coin, rolling a fair die, etc.

  7. Glivenko–Cantelli theorem - Wikipedia

    en.wikipedia.org/wiki/Glivenko–Cantelli_theorem

    The following theorem is central to statistical learning of binary classification tasks. Theorem (Vapnik and Chervonenkis, 1968) [8] Under certain consistency conditions, a universally measurable class of sets is a uniform Glivenko-Cantelli class if and only if it is a Vapnik–Chervonenkis class.

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  9. Characteristic function (probability theory) - Wikipedia

    en.wikipedia.org/wiki/Characteristic_function...

    In probability theory and statistics, the characteristic function of any real-valued random variable completely defines its probability distribution. If a random variable admits a probability density function, then the characteristic function is the Fourier transform (with sign reversal) of the probability density function.