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  2. Weibull distribution - Wikipedia

    en.wikipedia.org/wiki/Weibull_distribution

    In probability theory and statistics, the Weibull distribution / ˈ w aɪ b ʊ l / is a continuous probability distribution. It models a broad range of random variables, largely in the nature of a time to failure or time between events. Examples are maximum one-day rainfalls and the time a user spends on a web page.

  3. Kaniadakis Weibull distribution - Wikipedia

    en.wikipedia.org/wiki/Kaniadakis_Weibull...

    The Kaniadakis κ-Weibull distribution is exhibits power-law right tails, and it has the following probability density function: [3] = + ⁡ ()valid for , where | | < is the entropic index associated with the Kaniadakis entropy, > is the scale parameter, and > is the shape parameter or Weibull modulus.

  4. Discrete Weibull distribution - Wikipedia

    en.wikipedia.org/wiki/Discrete_Weibull_distribution

    The Discrete Weibull Distribution, first introduced by Toshio Nakagawa and Shunji Osaki, is a discrete analog of the continuous Weibull distribution, predominantly used in reliability engineering. It is particularly applicable for modeling failure data measured in discrete units like cycles or shocks.

  5. Weibull modulus - Wikipedia

    en.wikipedia.org/wiki/Weibull_modulus

    The Weibull modulus is a dimensionless parameter of the Weibull distribution. It represents the width of a probability density function (PDF) in which a higher modulus is a characteristic of a narrower distribution of values.

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  7. Relationships among probability distributions - Wikipedia

    en.wikipedia.org/wiki/Relationships_among...

    If X is an exponential random variable with mean β, then X 1/γ is a Weibull (γ, β) random variable. The square of a standard normal random variable has a chi-squared distribution with one degree of freedom. If X is a Student’s t random variable with ν degree of freedom, then X 2 is an F (1,ν) random variable.

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