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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.
In mathematics, the beta function, also called the Euler integral of the first kind, is a special function that is closely related to the gamma function and to binomial coefficients. It is defined by the integral. for complex number inputs such that . The beta function was studied by Leonhard Euler and Adrien-Marie Legendre and was given its ...
The gamma distribution is the maximum entropy probability distribution (both with respect to a uniform base measure and a base measure) for a random variable X for which E[X] = kθ = α/β is fixed and greater than zero, and E[ln X] = ψ(k) + ln θ = ψ(α) − ln β is fixed ( ψ is the digamma function ). [1]
Beta prime. In probability theory and statistics, the beta prime distribution (also known as inverted beta distribution or beta distribution of the second kind [1]) is an absolutely continuous probability distribution. If has a beta distribution, then the odds has a beta prime distribution.
In probability theory and statistics, the beta-binomial distribution is a family of discrete probability distributions on a finite support of non-negative integers arising when the probability of success in each of a fixed or known number of Bernoulli trials is either unknown or random. The beta-binomial distribution is the binomial ...
v. t. e. In probability theory and statistics, the Bernoulli distribution, named after Swiss mathematician Jacob Bernoulli, [1] is the discrete probability distribution of a random variable which takes the value 1 with probability and the value 0 with probability . Less formally, it can be thought of as a model for the set of possible outcomes ...
The exponential distribution is the special case of a Gamma distribution with shape parameter 1. If X ~ Exp (λ) and Xi ~ Exp (λ i) then: , closure under scaling by a positive factor. 1 + X ~ BenktanderWeibull (λ, 1), which reduces to a truncated exponential distribution. keX ~ Pareto ( k, λ). e−X ~ Beta (λ, 1).
Diagram of the Pearson system, showing distributions of types I, III, VI, V, and IV in terms of β 1 (squared skewness) and β 2 (traditional kurtosis) The Pearson distribution is a family of continuous probability distributions. It was first published by Karl Pearson in 1895 and subsequently extended by him in 1901 and 1916 in a series of ...