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  2. 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.

  3. Lévy flight - Wikipedia

    en.wikipedia.org/wiki/Lévy_flight

    A Lévy flight is a random walk in which the step-lengths have a stable distribution, [1] a probability distribution that is heavy-tailed. When defined as a walk in a space of dimension greater than one, the steps made are in isotropic random directions. Later researchers have extended the use of the term "Lévy flight" to also include cases ...

  4. Category:Tails of probability distributions - Wikipedia

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

    Heavy-tailed distribution; L. Long tail; Long-tail traffic; P. Pareto principle; Pickands–Balkema–De Haan theorem; Z. Zipf's law This page was last edited on 7 ...

  5. Long tail - Wikipedia

    en.wikipedia.org/wiki/Long_tail

    In statistics, the term long-tailed distribution has a narrow technical meaning, and is a subtype of heavy-tailed distribution. [2] [3] [4] Intuitively, a distribution is (right) long-tailed if, for any fixed amount, when a quantity exceeds a high level, it almost certainly exceeds it by at least that amount: large quantities are probably even ...

  6. Fat-tailed distribution - Wikipedia

    en.wikipedia.org/wiki/Fat-tailed_distribution

    A fat-tailed distribution is a probability distribution that exhibits a large skewness or kurtosis, relative to that of either a normal distribution or an exponential distribution. [ when defined as? ] In common usage, the terms fat-tailed and heavy-tailed are sometimes synonymous; fat-tailed is sometimes also defined as a subset of heavy-tailed.

  7. Power law - Wikipedia

    en.wikipedia.org/wiki/Power_law

    Mathematically, a strict power law cannot be a probability distribution, but a distribution that is a truncated power function is possible: () = for > where the exponent (Greek letter alpha, not to be confused with scaling factor used above) is greater than 1 (otherwise the tail has infinite area), the minimum value is needed otherwise the ...

  8. Skewness - Wikipedia

    en.wikipedia.org/wiki/Skewness

    negative skew: The left tail is longer; the mass of the distribution is concentrated on the right of the figure. The distribution is said to be left-skewed, left-tailed, or skewed to the left, despite the fact that the curve itself appears to be skewed or leaning to the right; left instead refers to the left tail being drawn out and, often, the ...

  9. Long-tail traffic - Wikipedia

    en.wikipedia.org/wiki/Long-tail_traffic

    Heavy-tail distributions have properties that are qualitatively different from commonly used (memoryless) distributions such as the exponential distribution. The Hurst parameter H is a measure of the level of self-similarity of a time series that exhibits long-range dependence, to which the heavy-tail distribution can be applied.