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In probability and statistics, the Kumaraswamy's double bounded distribution is a family of continuous probability distributions defined on the interval (0,1). It is similar to the beta distribution, but much simpler to use especially in simulation studies since its probability density function, cumulative distribution function and quantile functions can be expressed in closed form.
In probability theory, the Modified Kumaraswamy (MK) distribution is a two-parameter continuous probability distribution defined on the interval (0,1). It serves as an alternative to the beta and Kumaraswamy distributions for modeling double-bounded random variables.
The Kumaraswamy distribution is as versatile as the Beta distribution but has simple closed forms for both the cdf and the pdf. The logit metalog distribution, which is highly shape-flexible, has simple closed forms, and can be parameterized with data using linear least squares.
Marchenko–Pastur distribution; Matrix variate Dirichlet distribution; Modified half-normal distribution; Modified Kumaraswamy distribution; Multivariate Laplace distribution; Multivariate logistic distribution
Gamma distribution; Gamma/Gompertz distribution; Gaussian q-distribution; Generalised hyperbolic distribution; Generalized beta distribution; Generalized chi-squared distribution; Generalized Dirichlet distribution; Generalized extreme value distribution; Generalized gamma distribution; Generalized integer gamma distribution; Generalized ...
Kumaraswamy or Kumaraswami is an Indian male given name. It may also refer to: Murugan, also called Kumaraswami or Kartikeya, the Hindu god of war; Kumaraswamy distribution, a distribution form related to probability theory and statistics; Kumaraswamy Layout, a residential locality in southern Bangalore, India
In probability theory and statistics, the beta distribution is a family of continuous probability distributions defined on the interval [0, 1] or (0, 1) in terms of two positive parameters, denoted by alpha (α) and beta (β), that appear as exponents of the variable and its complement to 1, respectively, and control the shape of the distribution.
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