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  2. Head/tail breaks - Wikipedia

    en.wikipedia.org/wiki/Head/tail_breaks

    Head/tail breaks is a clustering algorithm for data with a heavy-tailed distribution such as power laws and lognormal distributions. The heavy-tailed distribution can be simply referred to the scaling pattern of far more small things than large ones, or alternatively numerous smallest, a very few largest, and some in between the smallest and ...

  3. Probability distribution - Wikipedia

    en.wikipedia.org/wiki/Probability_distribution

    A discrete probability distribution is applicable to the scenarios where the set of possible outcomes is discrete (e.g. a coin toss, a roll of a die) and the probabilities are encoded by a discrete list of the probabilities of the outcomes; in this case the discrete probability distribution is known as probability mass function.

  4. Category:Tails of probability distributions - Wikipedia

    en.wikipedia.org/wiki/Category:Tails_of...

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  5. Heavy-tailed distribution - Wikipedia

    en.wikipedia.org/wiki/Heavy-tailed_distribution

    The distribution of a random variable X with distribution function F is said to have a long right tail [1] if for all t > 0, [> + >] =,or equivalently ¯ (+) ¯ (). This has the intuitive interpretation for a right-tailed long-tailed distributed quantity that if the long-tailed quantity exceeds some high level, the probability approaches 1 that it will exceed any other higher level.

  6. Long tail - Wikipedia

    en.wikipedia.org/wiki/Long_tail

    The long-tail distribution applies at a given point in time, but over time the relative popularity of the sales of the individual products will change. [26] Although the distribution of sales may appear to be similar over time, the positions of the individual items within it will vary. For example, new items constantly enter most fashion markets.

  7. Q-function - Wikipedia

    en.wikipedia.org/wiki/Q-function

    In statistics, the Q-function is the tail distribution function of the standard normal distribution. [1] [2] In other words, () is the probability that a normal (Gaussian) random variable will obtain a value larger than standard deviations.

  8. Multivariate normal distribution - Wikipedia

    en.wikipedia.org/wiki/Multivariate_normal...

    The complementary cumulative distribution function (ccdf) or the tail distribution is defined as ...

  9. Sub-Gaussian distribution - Wikipedia

    en.wikipedia.org/wiki/Sub-Gaussian_distribution

    In probability theory, a subgaussian distribution, the distribution of a subgaussian random variable, is a probability distribution with strong tail decay. More specifically, the tails of a subgaussian distribution are dominated by (i.e. decay at least as fast as) the tails of a Gaussian. This property gives subgaussian distributions their name.