8 relations: Beta distribution, Beta function, Dirichlet distribution, Dirichlet-multinomial distribution, Lukacs's proportion-sum independence theorem, Multinomial distribution, Neutral vector, Statistics.
Beta distribution
In probability theory and statistics, the beta distribution is a family of continuous probability distributions defined on the interval parametrized by two positive shape parameters, denoted by α and β, that appear as exponents of the random variable and control the shape of the distribution.
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Beta function
In mathematics, the beta function, also called the Euler integral of the first kind, is a special function defined by for.
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Dirichlet distribution
In probability and statistics, the Dirichlet distribution (after Peter Gustav Lejeune Dirichlet), often denoted \operatorname(\boldsymbol\alpha), is a family of continuous multivariate probability distributions parameterized by a vector \boldsymbol\alpha of positive reals.
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Dirichlet-multinomial distribution
In probability theory and statistics, the Dirichlet-multinomial distribution is a family of discrete multivariate probability distributions on a finite support of non-negative integers.
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Lukacs's proportion-sum independence theorem
In statistics, Lukacs's proportion-sum independence theorem is a result that is used when studying proportions, in particular the Dirichlet distribution.
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Multinomial distribution
In probability theory, the multinomial distribution is a generalization of the binomial distribution.
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Neutral vector
In statistics, and specifically in the study of the Dirichlet distribution, a neutral vector of random variables is one that exhibits a particular type of statistical independence amongst its elements.
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Statistics
Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data.
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Redirects here:
Generalized dirichlet distribution.
References
[1] https://en.wikipedia.org/wiki/Generalized_Dirichlet_distribution