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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.
t-distribution – see Student's t-distribution (includes table) T distribution (disambiguation) t-statistic; Tag cloud – graphical display of info; Taguchi loss function; Taguchi methods; Tajima's D; Taleb distribution; Tampering (quality control) Taylor expansions for the moments of functions of random variables
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
Probability density functions of the order statistics for a sample of size n = 5 from an exponential distribution with unit scale parameter. In statistics, the kth order statistic of a statistical sample is equal to its kth-smallest value. [1]
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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.