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Beta distribution

The Beta distribution is a continuous probability distribution defined on the interval (0, 1) and parameterized by two positive shape parameters, commonly denoted as \alpha > 0 and \beta > 0, which control its shape and concentration around the mean. It serves as a flexible model for bounded random variables, such as proportions, probabilities, or fractions, and is particularly useful due to its conjugate prior properties in Bayesian inference for binomial likelihoods. The distribution arises naturally from the normalization of the product of two power functions and is closely related to the Beta function, B(\alpha, \beta) = \int_0^1 t^{\alpha-1}(1-t)^{\beta-1} \, dt, which appears in its normalizing constant. The probability density function of the Beta distribution is given by f(x; \alpha, \beta) = \frac{x^{\alpha-1} (1-x)^{\beta-1}}{B(\alpha, \beta)}, \quad 0 < x < 1. Its mean is \mu = \frac{\alpha}{\alpha + \beta} and variance is \sigma^2 = \frac{\alpha \beta}{(\alpha + \beta)^2 (\alpha + \beta + 1)}, with the mode at \frac{\alpha - 1}{\alpha + \beta - 2} for \alpha > 1, \beta > 1. These moments highlight the distribution's ability to produce U-shaped, J-shaped, unimodal, or uniform densities depending on the parameter values: for example, when \alpha = \beta = 1, it reduces to the uniform distribution on (0,1); equal \alpha = \beta > 1 yields symmetry around 0.5; and unequal values skew it toward 0 or 1. The Beta distribution also generalizes to forms with location and scale parameters, shifting and stretching the support to any finite interval (a, b). In statistical applications, the Beta distribution is widely employed as a prior for the success probability in binomial or Bernoulli models, enabling closed-form posterior updates in Bayesian analysis. It models task durations in project management via the PERT method, where parameters are estimated from optimistic, most likely, and pessimistic times to capture uncertainty in task durations. Additionally, it appears in reliability engineering for proportions of defective items, resource assessment for probabilistic evaluations within bounded intervals, and as a component in deriving other distributions like the F and t via transformations. Its computational tractability in software like R and its role in Dirichlet-multinomial models further underscore its importance in modern data analysis.

Definitions

Probability density function

The Beta distribution is a continuous probability distribution defined on the interval [0, 1] with two positive shape parameters \alpha > 0 and \beta > 0. The probability density function of a Beta-distributed random variable X \sim \text{Beta}(\alpha, \beta) is f(x; \alpha, \beta) = \frac{x^{\alpha-1} (1-x)^{\beta-1}}{B(\alpha, \beta)}, \quad 0 \leq x \leq 1, with f(x; \alpha, \beta) = 0 otherwise. Here, B(\alpha, \beta) denotes the Beta function, which acts as the normalizing constant ensuring that the density integrates to 1 over [0, 1]. The Beta function is defined by the integral B(\alpha, \beta) = \int_0^1 t^{\alpha-1} (1-t)^{\beta-1} \, dt and admits an alternative expression in terms of the Gamma function: B(\alpha, \beta) = \frac{\Gamma(\alpha) \Gamma(\beta)}{\Gamma(\alpha + \beta)}. At the boundaries, the density exhibits singular behavior when \alpha < 1, approaching infinity as x \to 0^+, and when \beta < 1, approaching infinity as x \to 1^-. In the special case \alpha = \beta = 1, the density simplifies to the uniform distribution on [0, 1], with f(x; 1, 1) = 1 for $0 \leq x \leq 1.

Cumulative distribution function

The cumulative distribution function (CDF) of the Beta distribution with shape parameters \alpha > 0 and \beta > 0 is given by F(x; \alpha, \beta) = \int_0^x f(t; \alpha, \beta) \, dt = I_x(\alpha, \beta), for $0 \leq x \leq 1, where f(t; \alpha, \beta) is the probability density function and I_x(\alpha, \beta) denotes the regularized incomplete beta function. The regularized incomplete beta function is defined as I_x(\alpha, \beta) = \frac{B(x; \alpha, \beta)}{B(\alpha, \beta)}, with the incomplete beta function B(x; \alpha, \beta) = \int_0^x t^{\alpha-1} (1-t)^{\beta-1} \, dt and the (complete) beta function B(\alpha, \beta) = \int_0^1 t^{\alpha-1} (1-t)^{\beta-1} \, dt = \frac{\Gamma(\alpha) \Gamma(\beta)}{\Gamma(\alpha + \beta)}. This representation follows directly from the fundamental theorem of calculus, as the PDF is the derivative of the CDF. The CDF satisfies F(0; \alpha, \beta) = 0 and F(1; \alpha, \beta) = 1, and is strictly increasing on [0, 1] because the PDF is positive and continuous on (0, 1) for \alpha > 0 and \beta > 0. No closed-form expression exists in terms of elementary functions, so numerical evaluation is required. The incomplete beta function can be computed using series expansions, such as the hypergeometric representation B_x(\alpha, \beta) = \frac{x^\alpha}{\alpha} \, {}_2F_1(\alpha, 1 - \beta; \alpha + 1; x), where {}_2F_1 is the Gauss hypergeometric function, or via continued fractions for efficient convergence in certain regions. Specifically, the continued fraction form for the regularized version converges rapidly when x < (\alpha + 1)/(\alpha + \beta + 2), expressed as I_x(\alpha, \beta) = \frac{x^\alpha (1 - x)^\beta}{\alpha B(\alpha, \beta)} \cfrac{1}{1 + d_1 \cfrac{1}{1 + d_2 \cfrac{1}{1 + \ddots}}}, with partial denominators d_{2m} = \frac{m(\beta - m)x}{(\alpha + 2m - 1)(\alpha + 2m)} and d_{2m+1} = -\frac{(\alpha + m)(\alpha + \beta + m)x}{(\alpha + 2m)(\alpha + 2m + 1)}. These methods ensure accurate computation for statistical applications involving the Beta distribution.

Parameterizations

The Beta distribution is most commonly parameterized using two shape parameters, denoted \alpha > 0 and \beta > 0, which control the shape of the distribution on the support interval [0, 1]. In this standard form, the probability density function is given by f(x; \alpha, \beta) = \frac{1}{B(\alpha, \beta)} x^{\alpha-1} (1-x)^{\beta-1}, \quad 0 < x < 1, where B(\alpha, \beta) = \int_0^1 t^{\alpha-1} (1-t)^{\beta-1} \, dt = \frac{\Gamma(\alpha) \Gamma(\beta)}{\Gamma(\alpha + \beta)} is the beta function. These parameters allow the distribution to model a wide range of shapes, from uniform (\alpha = \beta = 1) to highly skewed forms, and are conjugate priors for binomial likelihoods in Bayesian inference. An alternative parameterization expresses the shape parameters in terms of the mean \mu \in (0,1) and variance \sigma^2 < \mu(1-\mu). Here, \alpha = \mu \left( \frac{\mu(1-\mu)}{\sigma^2} - 1 \right) and \beta = (1-\mu) \left( \frac{\mu(1-\mu)}{\sigma^2} - 1 \right). This form is particularly useful when specifying the distribution based on moments, such as in project management or simulation studies where mean and variability are elicited directly. Closely related is the precision parameterization, which uses the mean \mu \in (0,1) and a precision parameter \phi > 0 (representing the total "sample size" or concentration). In this setup, \alpha = \mu \phi and \beta = (1-\mu) \phi, yielding variance \sigma^2 = \frac{\mu(1-\mu)}{\phi + 1}. Higher \phi values concentrate the distribution around \mu, making it suitable for modeling proportions with varying reliability, as in group-based trajectory models or beta regression. The four-parameter generalization extends the standard Beta to an arbitrary interval [a, b] with a < b, incorporating location a and scale b - a alongside shape parameters \alpha > 0 and \beta > 0. The probability density function becomes f(x; \alpha, \beta, a, b) = \frac{1}{B(\alpha, \beta) (b-a)^{\alpha + \beta - 1}} (x - a)^{\alpha - 1} (b - x)^{\beta - 1}, \quad a < x < b. This form, often called the four-parameter Beta or PERT distribution in project evaluation and review technique (PERT) applications, models bounded variables like task durations by shifting and scaling the standard Beta. In PERT, \alpha and \beta are sometimes further reparameterized using the mode or mean to align with optimistic, most likely, and pessimistic estimates. A niche parameterization arises in the context of order statistics from uniform samples. The k-th order statistic in a sample of size n from a Uniform[0,1] distribution follows a Beta(k, n - k + 1) distribution, providing a direct link to sample quantiles. This form highlights the Beta's role in non-parametric statistics and spacing distributions, where parameters reflect sample size and rank.

Properties

Mode

The mode of the Beta distribution, which represents the value of x in [0, 1] that maximizes the probability density function and serves as a measure of central tendency, is given by m = \frac{\alpha - 1}{\alpha + \beta - 2} when \alpha > 1 and \beta > 1. This formula arises from finding the critical point of the density by taking the derivative and setting it to zero. Specifically, the log-density is \log f(x) = C + (\alpha - 1) \log x + (\beta - 1) \log(1 - x), where C is a constant; differentiating yields \frac{d}{dx} \log f(x) = \frac{\alpha - 1}{x} - \frac{\beta - 1}{1 - x} = 0, which simplifies to the mode expression above. When \alpha \leq 1 and \beta > 1, the mode is at the boundary m = 0, as the density is non-increasing over [0, 1]. Conversely, if \beta \leq 1 and \alpha > 1, the mode is at m = 1. In the special case where \alpha = \beta = 1, the distribution is uniform on [0, 1], and every point is equally likely, so no unique mode exists. The Beta distribution has a single mode (unimodal) when at least one of \alpha or \beta is greater than or equal to 1: in the interior if both exceed 1, or at the corresponding boundary if one is less than 1 and the other exceeds 1. It is bimodal with modes at both boundaries when both parameters are less than 1. In the uniform case (\alpha = \beta = 1), every point is equally likely, so no unique mode exists. The location of the mode shifts toward 1 as \alpha increases relative to \beta, reflecting greater concentration of probability mass near the upper endpoint of the support.

Median

The median of a Beta distribution with shape parameters \alpha > 0 and \beta > 0, denoted m(\alpha, \beta), is defined as the value satisfying F(m(\alpha, \beta); \alpha, \beta) = \frac{1}{2}, where F(x; \alpha, \beta) is the cumulative distribution function given by the regularized incomplete beta function I_x(\alpha, \beta). No closed-form expression for the median exists in general, except in special cases such as when \alpha = 1 or \beta = 1, or when the distribution is symmetric. It is typically computed numerically by finding the inverse of the CDF. For the symmetric case where \alpha = \beta, the median is exactly m(\alpha, \alpha) = \frac{1}{2}, coinciding with both the mean and the mode. In skewed Beta distributions (where \alpha \neq \beta), the median provides a robust measure of central tendency, being less sensitive to the asymmetry than the mean, which is pulled toward the longer tail. This property follows the mode-median-mean inequality, where for right-skewed distributions (\alpha < \beta), mode \leq median \leq mean, and the reverse for left-skewed cases. A useful closed-form approximation for the median, valid for \alpha, \beta > 1, is m(\alpha, \beta) \approx \frac{\alpha - \frac{1}{3}}{\alpha + \beta - \frac{2}{3}}. This approximation arises as a refinement of the mode formula and exhibits relative errors below 4% for \alpha, \beta \geq 1, improving to under 1% for \alpha, \beta \geq 2, with errors decreasing as the parameters increase. For large \alpha + \beta, it converges closely to the true median and outperforms simpler alternatives like the mean. Numerical computation of the median often employs root-finding algorithms such as the bisection method or Newton-Raphson iteration on the equation I_x(\alpha, \beta) - \frac{1}{2} = 0, leveraging efficient implementations of the incomplete beta function. These methods are reliable for practical applications, especially in statistical software where the inverse CDF is a standard routine.

Mean

The expected value, or mean, of a Beta-distributed random variable X \sim \text{Beta}(\alpha, \beta) with shape parameters \alpha > 0 and \beta > 0 is given by \mu = \mathbb{E}[X] = \frac{\alpha}{\alpha + \beta}. This formula arises from the first moment of the distribution. Specifically, \mathbb{E}[X] = \int_0^1 x f(x; \alpha, \beta) \, dx, where f(x; \alpha, \beta) = \frac{x^{\alpha-1} (1-x)^{\beta-1}}{B(\alpha, \beta)} is the probability density function and B(\alpha, \beta) = \frac{\Gamma(\alpha) \Gamma(\beta)}{\Gamma(\alpha + \beta)} is the beta function. The integral evaluates to \mathbb{E}[X] = \frac{B(\alpha + 1, \beta)}{B(\alpha, \beta)} = \frac{\Gamma(\alpha + 1) \Gamma(\beta) / \Gamma(\alpha + \beta + 1)}{\Gamma(\alpha) \Gamma(\beta) / \Gamma(\alpha + \beta)}. Applying the Gamma function recurrence \Gamma(z + 1) = z \Gamma(z) twice yields \frac{\Gamma(\alpha + 1)}{\Gamma(\alpha)} = \alpha and \frac{\Gamma(\alpha + \beta)}{\Gamma(\alpha + \beta + 1)} = \frac{1}{\alpha + \beta}, simplifying the expression to \frac{\alpha}{\alpha + \beta}. The mean \mu interprets as the long-run average proportion or success probability in processes modeled by the Beta distribution, which always falls in the interval (0, 1) for finite positive parameters. In Bayesian analysis, the Beta distribution acts as a conjugate prior for the binomial likelihood, where the prior mean \frac{\alpha}{\alpha + \beta} updates to the posterior mean \frac{\alpha + s}{\alpha + \beta + n} after observing s successes in n trials, providing a weighted balance between prior belief and data. When parameters are known, this mean is the exact central tendency; in Bayesian estimation, the posterior mean is unbiased under squared error loss.

Variance

The variance of a Beta-distributed random variable X \sim \operatorname{Beta}(\alpha, \beta) with shape parameters \alpha > 0 and \beta > 0 is \operatorname{Var}(X) = \frac{\alpha \beta}{(\alpha + \beta)^2 (\alpha + \beta + 1)}. This formula quantifies the dispersion around the mean \mu = \alpha / (\alpha + \beta), and it can equivalently be expressed as \operatorname{Var}(X) = \mu (1 - \mu) / (\alpha + \beta + 1). To derive this, first compute the second raw moment using the relation to the Beta function: E[X^2] = B(\alpha + 2, \beta) / B(\alpha, \beta) = \frac{\alpha (\alpha + 1)}{(\alpha + \beta) (\alpha + \beta + 1)}, where B(a, b) = \Gamma(a) \Gamma(b) / \Gamma(a + b). Then, apply the variance definition \operatorname{Var}(X) = E[X^2] - (E[X])^2, substituting E[X] = \alpha / (\alpha + \beta) to yield the formula above. The variance achieves its maximum value of $1/12 when \alpha = \beta = 1, corresponding to the uniform distribution on [0, 1]. For a fixed mean \mu, the variance decreases as \alpha + \beta increases, reflecting greater concentration around \mu.

Skewness

The skewness of the Beta distribution, denoted \gamma_1, quantifies the asymmetry of its probability density function around the mean. It is defined as the third standardized central moment: \gamma_1 = \frac{\mu_3}{\sigma^3}, where \mu_3 = \mathbb{E}[(X - \mu)^3] is the third central moment, \mu = \mathbb{E}[X] = \frac{\alpha}{\alpha + \beta} is the mean, and \sigma^2 = \mathrm{Var}(X) = \frac{\alpha \beta}{(\alpha + \beta)^2 (\alpha + \beta + 1)} is the variance. To derive \gamma_1, first compute the raw moments of the Beta distribution, given by \mathbb{E}[X^k] = \frac{B(\alpha + k, \beta)}{B(\alpha, \beta)} = \prod_{i=0}^{k-1} \frac{\alpha + i}{\alpha + \beta + i} for positive integer k, where B is the beta function. The third raw moment is thus \mathbb{E}[X^3] = \frac{\alpha (\alpha + 1) (\alpha + 2)}{(\alpha + \beta) (\alpha + \beta + 1) (\alpha + \beta + 2)}. The second raw moment is \mathbb{E}[X^2] = \mu^2 + \sigma^2. Substituting into the expansion \mu_3 = \mathbb{E}[X^3] - 3 \mu \mathbb{E}[X^2] + 2 \mu^3 yields \mu_3 = \frac{2 \alpha \beta (\beta - \alpha)}{(\alpha + \beta)^3 (\alpha + \beta + 1) (\alpha + \beta + 2)}. Dividing by \sigma^3 then gives the skewness: \gamma_1 = \frac{2 (\beta - \alpha) \sqrt{\alpha + \beta + 1}}{(\alpha + \beta + 2) \sqrt{\alpha \beta}}. The sign of \gamma_1 indicates the direction of asymmetry: \gamma_1 > 0 when \alpha < \beta (positive skew, with a longer right tail), \gamma_1 < 0 when \alpha > \beta (negative skew, with a longer left tail), and \gamma_1 = 0 when \alpha = \beta (symmetric case). The absolute value |\gamma_1| is bounded, with details on the bound provided in the relationships between measures section. This measure of asymmetry is particularly useful for the Beta distribution in modeling proportions or probabilities, where the shape parameters \alpha and \beta reflect differing influences that introduce skewness.

Kurtosis

The kurtosis \kappa of a random variable X following the Beta distribution with shape parameters \alpha > 0 and \beta > 0 is defined as the fourth standardized central moment, \kappa = \mathbb{E}[(X - \mu)^4] / \sigma^4, where \mu = \alpha / (\alpha + \beta) is the mean and \sigma^2 = \alpha \beta / [(\alpha + \beta)^2 (\alpha + \beta + 1)] is the variance. The excess kurtosis, \gamma_2 = \kappa - 3, quantifies the distribution's peakedness and tail heaviness relative to the normal distribution and is given by the exact formula \gamma_2 = \frac{6 \left[ \alpha^3 + \alpha^2 (1 - 2\beta) + \beta^2 (1 + \beta) - 2 \alpha \beta (2 + \beta) \right] }{\alpha \beta (\alpha + \beta + 2) (\alpha + \beta + 3)}. This expression is derived from the fourth central moment \mu_4 = \mathbb{E}[(X - \mu)^4], which can be computed using the raw moments \mathbb{E}[X^r] = B(\alpha + r, \beta) / B(\alpha, \beta) for positive integer r, where B is the beta function, combined with the binomial theorem to expand the central moment in terms of raw moments up to the fourth order. The Beta distribution is always platykurtic relative to the normal distribution for finite \alpha, \beta > 0, with \gamma_2 < 0, indicating lighter tails and a lower peak than the normal. The excess kurtosis approaches 0 from below as \alpha, \beta \to \infty, converging to the mesokurtic normal distribution in the limit. In the opposite extreme, as \alpha = \beta \to 0^+, \gamma_2 \to -2, reflecting the highly bimodal, U-shaped density with the heaviest relative tails possible for the family. For the uniform case \alpha = \beta = 1, \gamma_2 = -1.2, exemplifying moderate platykurtosis.

Characteristic function

The characteristic function of a Beta-distributed random variable X \sim \operatorname{Beta}(\alpha, \beta) with shape parameters \alpha > 0 and \beta > 0 is defined as \phi_X(t) = \mathbb{E}[e^{itX}] and given by \phi_X(t) = {}_1F_1(\alpha; \alpha + \beta; it), where {}_1F_1 denotes the confluent hypergeometric function of the first kind. The moment-generating function M_X(t) = \mathbb{E}[e^{tX}] is obtained as the analytic continuation of the characteristic function by replacing it with t, yielding M_X(t) = {}_1F_1(\alpha; \alpha + \beta; t) for real t in the domain of convergence. This form arises from the direct evaluation of the defining integral \phi_X(t) = \int_0^1 e^{itx} f(x; \alpha, \beta) \, dx = \frac{1}{B(\alpha, \beta)} \int_0^1 e^{itx} x^{\alpha-1} (1-x)^{\beta-1} \, dx, where f(x; \alpha, \beta) is the probability density function and B(\alpha, \beta) is the beta function; expanding the exponential in its Taylor series and integrating term by term produces the hypergeometric series representation {}_1F_1(\alpha; \alpha + \beta; it) = \sum_{k=0}^\infty \frac{(\alpha)_k}{(\alpha + \beta)_k} \frac{(it)^k}{k!}, with (\cdot)_k denoting the Pochhammer symbol. The characteristic and moment-generating functions enable the computation of all moments of the distribution through differentiation: the nth raw moment is \mathbb{E}[X^n] = M_X^{(n)}(0), where M_X^{(n)} is the nth derivative. Additionally, the characteristic function facilitates the analysis of convolutions and sums involving Beta-distributed variables via the product of their characteristic functions.

Entropy

The differential entropy h of a Beta-distributed random variable X \sim \operatorname{Beta}(\alpha, \beta) with \alpha > 0 and \beta > 0 is defined as h = -\int_0^1 f(x) \log f(x) \, dx, where f(x) = \frac{x^{\alpha-1} (1-x)^{\beta-1}}{B(\alpha, \beta)} is the probability density function and B(\alpha, \beta) = \frac{\Gamma(\alpha) \Gamma(\beta)}{\Gamma(\alpha + \beta)} is the beta function. This entropy measures the average uncertainty or information content in the distribution and evaluates to the closed-form expression h = \log B(\alpha, \beta) - (\alpha - 1) \psi(\alpha) - (\beta - 1) \psi(\beta) + (\alpha + \beta - 2) \psi(\alpha + \beta), where \psi(z) = \frac{d}{dz} \log \Gamma(z) denotes the digamma function. To derive this formula, start with h = -\mathbb{E}[\log f(X)]. Substituting the density yields \log f(x) = (\alpha - 1) \log x + (\beta - 1) \log(1 - x) - \log B(\alpha, \beta), so h = -(\alpha - 1) \mathbb{E}[\log X] - (\beta - 1) \mathbb{E}[\log(1 - X)] + \log B(\alpha, \beta). The required expectations are \mathbb{E}[\log X] = \psi(\alpha) - \psi(\alpha + \beta) and \mathbb{E}[\log(1 - X)] = \psi(\beta) - \psi(\alpha + \beta), which follow from the relation to the derivative of the log-gamma function in the normalizing constant of the density. This entropy achieves its maximum value of 0 when \alpha = \beta = 1, corresponding to the uniform distribution on [0, 1], where f(x) = 1 and h = -\int_0^1 \log 1 \, dx = 0. For fixed ratio \alpha / \beta, the entropy decreases as \alpha + \beta increases, since the distribution sharpens toward a Dirac delta at the mode, reducing uncertainty. Variants such as the Rényi entropy generalize this measure but are less commonly applied to the Beta distribution compared to the Shannon (differential) form.

Relationships between measures

The relationships among the central tendency measures of the Beta distribution provide insight into its skewness and symmetry. For shape parameters \alpha \geq 1 and \beta \geq 1, the distribution is unimodal. When \alpha = \beta, the distribution is symmetric about $1/2, and the mean, median, and mode coincide at $1/2. When \alpha > \beta, the distribution exhibits negative skewness, satisfying mode \geq median \geq mean, with strict inequality unless \alpha = \beta. By symmetry, when \alpha < \beta, the distribution is positively skewed, satisfying mean \geq median \geq mode, with strict inequality unless \alpha = \beta. The arithmetic mean \mu, geometric mean G, and harmonic mean H of the Beta distribution obey the inequality \mu \geq G \geq H, with equality if and only if \alpha = \beta. The geometric mean is given by G = \exp\left(\psi(\alpha) - \psi(\alpha + \beta)\right), where \psi denotes the digamma function. The harmonic mean (requiring \alpha > 1) is H = \frac{\alpha - 1}{\alpha + \beta - 1}. The variance \sigma^2 can be expressed in terms of the mean as \sigma^2 = \frac{\mu(1 - \mu)}{\alpha + \beta + 1}. This parameterization highlights how the variance decreases as the total shape \alpha + \beta increases for fixed \mu. The skewness \gamma of the Beta distribution is zero if and only if \alpha = \beta, corresponding to the symmetric case. For the Beta distribution, the excess kurtosis \kappa - 3 and skewness \gamma satisfy the inequality \kappa - 3 \geq \frac{6}{5} \gamma^2, which represents the lower boundary of the feasible region in the kurtosis-skewness plane for this family; equality holds in limiting cases as one shape parameter approaches zero while the other diverges. This bound, adapted from Pearson's inequality, characterizes the minimal peakedness for a given degree of asymmetry achievable by Beta distributions.

Symmetry

The Beta distribution is symmetric around its mean of 1/2 if and only if the shape parameters satisfy α = β. Under this condition, the probability density function obeys f(x; α, α) = f(1 - x; α, α) for all x ∈ [0, 1], meaning the density is a mirror image across the line x = 1/2. For a random variable Y following a Beta(α, β) distribution, the reflected variable 1 - Y follows a Beta(β, α) distribution. This reflection property interchanges the roles of α and β, causing the skewness to flip sign: the distribution skewed positively (right tail longer) when α < β becomes negatively skewed (left tail longer) upon transformation, and vice versa. When α = β, the symmetry implies that all odd-order central moments about the mean 1/2 vanish. Specifically, letting Z = Y - 1/2, the distribution of Z is symmetric about 0 (Z and -Z are identically distributed), ensuring E[Z^{2k+1}] = 0 for any nonnegative integer k. This symmetry makes the Beta(α, α) distribution suitable for modeling proportions without inherent directional bias, such as equally likely outcomes in Bayesian priors or balanced allocation problems. Asymmetric Beta(α, β) distributions with α ≠ β, however, are preferred for scenarios involving directional biases, like overweighting success or failure probabilities in proportion-based models.

Geometry of the PDF

The probability density function (PDF) of the Beta distribution, defined on the interval (0, 1), displays a diverse range of geometric forms governed by the shape parameters \alpha > 0 and \beta > 0. These shapes reflect the distribution's flexibility in modeling bounded phenomena, such as proportions or probabilities. When both \alpha < 1 and \beta < 1, the PDF adopts a U-shaped configuration, featuring elevated densities near the endpoints x = 0 and x = 1, with a local minimum in the interior; this form arises because the exponents \alpha - 1 < 0 and \beta - 1 < 0 cause the function to diverge at the boundaries while dipping centrally. In contrast, a J-shaped profile emerges when \alpha < 1 and \beta > 1 (or vice versa), resulting in a monotonic decreasing (or increasing) curve that approaches infinity at one endpoint and diminishes to zero at the other, emphasizing mass near one boundary. For \alpha > 1 and \beta > 1, the PDF is unimodal, forming a single-peaked, bell-like curve with the maximum in the open interval, suitable for representing concentrated probabilities away from the edges. Special cases further illustrate these geometric properties. The uniform distribution on [0, 1] occurs at \alpha = \beta = 1, yielding a flat, constant PDF of height 1. At \alpha = \beta = 1/2, the PDF corresponds to the arcsine distribution, exhibiting an extreme U-shape with singularities at both endpoints and a deep central trough. As \alpha \to \infty with \beta fixed (or vice versa), the PDF concentrates sharply near x = 1 (or x = 0), approximating a Dirac delta function at that point. Regarding concavity, the Beta PDF's curvature varies with \alpha and \beta, influencing its visual smoothness. For \alpha > 1 and \beta > 1, the function is concave downward near the mode, creating the typical "hump" of unimodal distributions, while potentially exhibiting concave upward regions in the tails. The number of inflection points, where the second derivative f''(x) = 0, is at most two; specifically, when both \alpha > 2 and \beta > 2, two such points exist, marking transitions from concave upward near the boundaries to concave downward around the mode and back to concave upward. These points are located by solving the equation for the second derivative f''(x) = 0, which yields complex closed-form expressions involving square roots and generally requires numerical evaluation for specific parameters. For smaller parameters in the unimodal regime (1 < \alpha, \beta ≤ 2), the PDF may lack inflections or show only one, maintaining overall concavity without multiple curvature changes.

Related distributions

Transformations

If X \sim \text{Beta}(\alpha, \beta), the transformation Y = \frac{X}{1 - X} yields Y \sim \text{Beta-prime}(\alpha, \beta), mapping the support from (0, 1) to (0, \infty). The Beta-prime distribution arises in Bayesian analysis for modeling odds ratios and is related to the F-distribution via Y \stackrel{d}{=} \frac{\alpha}{\beta} F(2\alpha, 2\beta), where F denotes an F-distributed random variable with degrees of freedom $2\alpha and $2\beta. The logit transformation Z = \log\left( \frac{X}{1 - X} \right) extends the support to (-\infty, \infty), facilitating modeling on the unbounded real line. The resulting density is f_Z(z) = \frac{e^{\alpha z}}{B(\alpha, \beta) (1 + e^z)^{\alpha + \beta}}, \quad z \in \mathbb{R}, known as the type IV generalized logistic distribution (or logistic-beta distribution). In the special case \alpha = \beta = 1, where X is uniform on (0, 1), Z follows the standard logistic distribution with location 0 and scale 1. Power transformations Y = X^\gamma for \gamma > 0 do not generally yield another Beta distribution except when \gamma = 1. The density of Y is f_Y(y) = \frac{1}{\gamma y^{1 - 1/\gamma}} \frac{y^{(\alpha - 1)/\gamma} (1 - y^{1/\gamma})^{\beta - 1}}{B(\alpha, \beta)}, \quad 0 < y < 1, which lacks the Beta form due to the nonlinear term in (1 - y^{1/\gamma})^{\beta - 1}. The arcsine transform provides variance stabilization, particularly useful for Beta-distributed proportions approximating binomial data. For the special case \alpha = \beta = 1/2 (the arcsine distribution), Y = \sqrt{X} has density f_Y(y) = \frac{2}{\pi \sqrt{1 - y^2}}, \quad 0 < y < 1. In general, the transform \arcsin(\sqrt{X}) (or the Freeman-Tukey variant \arcsin(\sqrt{X}) + \arcsin(\sqrt{1 - X})) stabilizes variance, with asymptotic normality \sqrt{n} \left( \arcsin(\sqrt{X/n}) - \arcsin(\sqrt{\mu}) \right) \to N(0, 1/4) for binomial-related Beta models, where \mu = \alpha/(\alpha + \beta). These transformations are valuable for extending the Beta distribution's applicability: the ratio and logit map to positive reals or the full real line for unbounded modeling, while power and arcsine variants aid in stabilizing variance or generating related distributions like the Beta-prime (detailed further in special cases).

Special cases

The Beta distribution encompasses several notable special cases defined by specific values of the shape parameters \alpha and \beta. When \alpha = \beta = 1, it reduces to the uniform distribution on the interval [0, 1], with probability density function (PDF) f(x) = 1, \quad 0 \leq x \leq 1. This case yields a constant density across the support, representing complete ignorance or uniformity over the interval. Another prominent special case arises when \alpha = \beta = \frac{1}{2}, resulting in the arcsine distribution, which has the PDF f(x) = \frac{1}{\pi \sqrt{x(1-x)}}, \quad 0 < x < 1. The density exhibits a U-shaped form, concentrating near the boundaries 0 and 1 while approaching infinity at those endpoints, reflecting high uncertainty at the extremes. This distribution is proper, with total integral equal to 1, and serves as the Jeffreys prior for Bayesian inference on a binomial proportion parameter, providing an objective, non-informative conjugate prior (detailed in Choice of priors). The power-function distribution emerges as a special case when one shape parameter is 1 and the other is positive but not necessarily 1; for instance, with \beta = 1 and \alpha > 0, the PDF simplifies to f(x) = \alpha x^{\alpha - 1}, \quad 0 < x < 1. This form produces a monotonically increasing density if \alpha > 1 (skewed right) or decreasing if $0 < \alpha < 1 (skewed left), often used to model phenomena with power-law behavior on a bounded interval. The symmetric case with \alpha = 1 and \beta > 0 yields f(x) = \beta (1 - x)^{\beta - 1}, similarly skewed but toward 0. Limiting cases of the Beta distribution occur as the shape parameters approach boundary values. As \alpha \to 0^+ with \beta > 0 fixed, the distribution concentrates all mass at 0, approaching a Dirac delta function \delta(x) at the lower endpoint. Conversely, as \alpha \to \infty with \beta > 0 fixed, it concentrates at 1, yielding \delta(x - 1). More generally, when \alpha = \beta \to \infty while holding the mean \mu = \frac{\alpha}{\alpha + \beta} fixed at some p \in (0,1), the distribution converges to a Dirac delta \delta(x - p) centered at p. These limits highlight the flexibility of the Beta family in approximating degenerate distributions under extreme parameterizations.

Derivations from other distributions

The Beta distribution arises as the posterior distribution in Bayesian inference for the binomial model. Specifically, if a Beta(\alpha, \beta) prior is placed on the success probability \theta of a binomial likelihood, and data consisting of s successes in n independent trials is observed, then the posterior distribution of \theta is Beta(\alpha + s, \beta + n - s). This conjugacy property makes the Beta distribution particularly suitable for modeling proportions in Bayesian settings. Another derivation connects the Beta distribution to the Gamma family. Let U \sim \Gamma(\alpha, 1) and V \sim \Gamma(\beta, 1) be independent random variables, where \Gamma(\alpha, \theta) denotes the Gamma distribution with shape \alpha and rate \theta. Then, the ratio X = U / (U + V) follows a Beta(\alpha, \beta) distribution. This representation highlights the Beta as a distribution on the unit interval derived from positive-valued variables normalized to sum to unity. The Beta distribution also emerges in the context of order statistics from the uniform distribution. For a sample of m independent Uniform(0,1) random variables, the k-th order statistic (the k-th smallest value) follows a Beta(k, m - k + 1) distribution. This connection underscores the Beta's role in describing spacings and quantiles within uniform samples. Furthermore, the Beta distribution is the marginal distribution of components in the Dirichlet distribution. If \mathbf{Y} = (Y_1, \dots, Y_k) follows a Dirichlet(\alpha_1, \dots, \alpha_k) distribution, then the marginal distribution of Y_i is Beta(\alpha_i, \sum_{j \neq i} \alpha_j). The Dirichlet, as a multivariate generalization of the Beta, thus yields the Beta as its one-dimensional projection for individual proportions. These derivations collectively demonstrate the Beta distribution's natural emergence in scenarios involving proportions, normalized ratios, and ordered samples, positioning it as a foundational model for bounded random variables on [0,1].

Combinations and compoundings

The beta-binomial distribution arises as a compound distribution when the success probability p in a binomial distribution with n trials follows a beta distribution with shape parameters \alpha > 0 and \beta > 0. Specifically, if p \sim \mathrm{Beta}(\alpha, \beta) and, conditional on p, X \mid p \sim \mathrm{Binomial}(n, p), then the marginal distribution of X is beta-binomial with parameters n, \alpha, and \beta. The probability mass function (PMF) is given by P(X = k) = \binom{n}{k} \frac{\alpha^{(k)} \beta^{(n-k)}}{(\alpha + \beta)^{(n)}}, \quad k = 0, 1, \dots, n, where (\cdot)^{(m)} denotes the rising factorial (Pochhammer symbol) (x)^{(m)} = x(x+1)\cdots(x+m-1). This form was originally derived in the context of Pólya's urn model, introduced by Eggenberger and Pólya in 1923 to describe contagious processes such as the spread of infectious diseases, where drawing a ball reinforces the urn by adding another of the same color, yielding exchangeable sequences whose count distribution is beta-binomial. The beta-binomial exhibits overdispersion relative to the binomial, with variance n \frac{\alpha + \beta + n}{\alpha + \beta + 1} \cdot \frac{\alpha \beta}{(\alpha + \beta)^2} > n p (1 - p) where p = \alpha / (\alpha + \beta), allowing it to model extra variability in success counts. Marginally integrating over the beta prior on p yields a distribution akin to a hypergeometric form in finite urn sampling limits. The beta-negative binomial distribution similarly compounds a negative binomial distribution with a beta prior on the success probability p. If p \sim \mathrm{Beta}(\alpha, \beta) and, conditional on p, X \mid p \sim \mathrm{NegativeBinomial}(r, p) (number of failures before r successes), then the marginal X follows a beta-negative binomial distribution with parameters r, \alpha, and \beta. This was first documented by Kemp and Kemp in the 1950s using methods analogous to generalized hypergeometric distributions. The PMF involves rising factorials in a form parallel to the beta-binomial, specifically P(X = k) = \frac{\Gamma(r + k)}{k! \Gamma(r)} \frac{B(\alpha + r, \beta + k)}{B(\alpha, \beta)}, for k = 0, 1, 2, \dots. Like the beta-binomial, it introduces overdispersion for modeling counts of failures until a fixed number of successes, such as in reliability or ecology, where heterogeneity in p exceeds binomial assumptions. The beta-Poisson distribution results from compounding a Poisson distribution with rate \lambda p, where p \sim \mathrm{Beta}(\alpha, \beta). If p \sim \mathrm{Beta}(\alpha, \beta) and X \mid p \sim \mathrm{Poisson}(\lambda p), the marginal X is beta-Poisson with parameters \lambda > 0, \alpha > 0, and \beta > 0. This model, originally proposed by Furumoto and Mickey in 1967 for infectivity-dilution curves in virology, captures under- or over-dispersion in count data by allowing the effective rate to vary according to the beta mixing distribution. The PMF is P(X = k) = e^{-\lambda} \frac{(\lambda)^k}{k!} \, _1F_1(k + \alpha; k + \alpha + \beta; \lambda), where _1F_1 is the confluent hypergeometric function, but it is frequently approximated or used in dose-response contexts for its flexibility beyond the standard Poisson. The variance exceeds the mean \lambda \alpha / (\alpha + \beta), providing a mechanism for modeling clustered or heterogeneous events, such as in microbial risk assessment. These compound distributions generally introduce overdispersion due to the variability in the beta-distributed parameter, enabling better fits to real-world data with unobserved heterogeneity compared to their non-compounded counterparts. In Bayesian modeling, the beta prior's conjugacy facilitates posterior inference for such mixtures.

Generalizations

The Dirichlet distribution serves as the primary multivariate generalization of the Beta distribution, extending it from the unit interval to the (k-1)-simplex for k ≥ 2 components that sum to unity. Defined with positive parameters \alpha_1, \dots, \alpha_k > 0, its probability density function is given by f(\mathbf{x}) = \frac{\Gamma\left(\sum_{i=1}^k \alpha_i\right)}{\prod_{i=1}^k \Gamma(\alpha_i)} \prod_{i=1}^k x_i^{\alpha_i - 1}, \quad x_i > 0, \ \sum_{i=1}^k x_i = 1, where \Gamma denotes the gamma function and the normalizing constant is the multivariate beta function B(\boldsymbol{\alpha}) = \prod_{i=1}^k \Gamma(\alpha_i) / \Gamma(\sum_{i=1}^k \alpha_i). When k=2, the Dirichlet reduces exactly to the Beta distribution with parameters \alpha_1 and \alpha_2. A key property is that the marginal distribution of any single component X_i follows a Beta distribution with parameters \alpha_i and \sum_{j \neq i} \alpha_j. This makes the Dirichlet particularly suitable for modeling compositional data, where observations represent relative proportions summing to a constant, as introduced in the log-ratio framework for such data. The beta prime distribution, also called the beta distribution of the second kind, extends the support of the Beta distribution from (0,1) to (0, ∞) via the transformation Y = X / (1 - X) where X \sim \mathrm{Beta}(\alpha, \beta) with \alpha, \beta > 0. Its density is f(y) = \frac{y^{\alpha-1} (1 + y)^{-(\alpha + \beta)}}{B(\alpha, \beta)}, \quad y > 0. This distribution arises naturally in Bayesian contexts and ratio modeling, and serves as a building block for further extensions. Generalized beta distributions introduce additional shape parameters to enhance flexibility beyond the standard two-parameter form, often incorporating power transformations or hypergeometric functions in the density. The generalized beta distribution of the first kind (GB1), proposed by McDonald and Xu, adds parameters a > 0 for tail behavior and b > 0 for scaling, with density f(x; a, b, p, q) = \frac{|a| x^{a p - 1} \left[1 - (1 - c) (x/b)^a \right]^{q - 1}}{b^{a p} B(p, q) \left[1 + c (x/b)^a \right]^{p + q}}, \quad 0 < x < b, where c = 1 yields a form encompassing the standard Beta as a special case when a = 1, b = 1, c = 0. Similarly, the generalized beta of the second kind (GB2), developed by McDonald, includes four shape parameters a, b, p, q > 0 and supports (0, ∞), with density proportional to y^{a p - 1} (1 + y)^{ - a (p + q) } (1 + (y/b)^a )^{ - b q }; it generalizes the beta prime when a = b = 1. These forms are widely applied in econometrics for income and size distributions due to their ability to capture skewness and heavy tails. Further generalizations incorporate hypergeometric functions directly into the density for added versatility in Bayesian priors and predictive modeling. The Gauss hypergeometric (GH) distribution, introduced by Armero and Bayarri, has density f(x; p, q, r, \lambda) = \frac{x^{p-1} (1 - x)^{q-1} (1 + \lambda x)^{-r}}{B(p, q) \ {}_2F_1(r, p; p+q; -\lambda)}, \quad 0 < x < 1, where {}_2F_1 is the Gauss hypergeometric function; it reduces to the Beta when \lambda = 0 or r = 0. This allows for more nuanced tail control in queueing and reliability applications. The compound confluent hypergeometric (CCH) distribution unifies the GH, GB, and other forms by adding exponential and additional scaling terms, providing a six-parameter family that further broadens the Beta's applicability in regression and conditioning scenarios.

Parameter estimation

Method of moments

The method of moments provides a straightforward approach to estimate the parameters \alpha and \beta of the Beta distribution by matching the first two population moments to their sample counterparts. The population mean is given by \mu = \frac{\alpha}{\alpha + \beta}, which is equated to the sample mean \bar{x}. Similarly, the population variance is \sigma^2 = \frac{\alpha \beta}{(\alpha + \beta)^2 (\alpha + \beta + 1)}, set equal to the sample variance s^2. These equations leverage the known relationships between the moments and parameters, allowing for direct algebraic solution without optimization. Solving the system yields the estimators: \hat{\alpha} = \bar{x} \left( \frac{\bar{x}(1 - \bar{x})}{s^2} - 1 \right), \quad \hat{\beta} = (1 - \bar{x}) \left( \frac{\bar{x}(1 - \bar{x})}{s^2} - 1 \right). Equivalently, the second raw moment m_2 = \frac{\alpha(\alpha + 1)}{(\alpha + \beta)(\alpha + \beta + 1)} can be used in place of the variance, leading to the same expressions since s^2 = m_2 - \bar{x}^2. These closed-form solutions make the method computationally simple and intuitive, particularly when sample moments are readily available from data. Although higher-order moments could enhance robustness in certain scenarios, the first two suffice for the two-parameter family, as they uniquely determine \alpha and \beta. The estimators are consistent, converging in probability to the true parameters as the sample size n \to \infty, due to the consistency of the sample mean and variance. However, they exhibit bias for small samples, leading to inefficiency compared to other methods in low-data regimes.

Maximum likelihood estimation

The maximum likelihood estimation (MLE) for the shape parameters \alpha > 0 and \beta > 0 of the Beta distribution is based on maximizing the likelihood function for an independent and identically distributed sample x_1, \dots, x_n drawn from Beta(\alpha, \beta), where each x_i \in (0,1). The corresponding log-likelihood function is l(\alpha, \beta) = \sum_{i=1}^n \bigl[ (\alpha - 1) \log x_i + (\beta - 1) \log (1 - x_i) \bigr] - n \log B(\alpha, \beta), with B(\alpha, \beta) = \Gamma(\alpha) \Gamma(\beta) / \Gamma(\alpha + \beta) denoting the beta function. To obtain the MLEs \hat{\alpha} and \hat{\beta}, the partial derivatives of the log-likelihood (known as the score equations) are set to zero: \frac{\partial l}{\partial \alpha} = \sum_{i=1}^n \log x_i - n \bigl[ \psi(\alpha) - \psi(\alpha + \beta) \bigr] = 0, \frac{\partial l}{\partial \beta} = \sum_{i=1}^n \log (1 - x_i) - n \bigl[ \psi(\beta) - \psi(\alpha + \beta) \bigr] = 0, where \psi(\cdot) is the digamma function defined as the derivative of the log-gamma function, \psi(z) = \frac{d}{dz} \log \Gamma(z). These nonlinear equations lack a closed-form solution and require numerical optimization. The Newton-Raphson method is a standard iterative approach, updating parameter estimates via \boldsymbol{\theta}^{(k+1)} = \boldsymbol{\theta}^{(k)} - H^{-1} S, where \boldsymbol{\theta} = (\alpha, \beta)^T, S is the score vector, and H is the Hessian matrix of second partial derivatives. Initial values can be obtained from method-of-moments estimates. Alternatively, the expectation-maximization (EM) algorithm may be applied, treating the parameters in a latent variable framework related to the gamma representation of the Beta distribution, though it is less common for uncensored complete samples. Convergence is typically rapid for interior points, but care is needed with starting values to avoid local maxima. Asymptotically, as n \to \infty, the MLE (\hat{\alpha}, \hat{\beta}) is normally distributed with mean (\alpha, \beta) and variance-covariance matrix n^{-1} I(\alpha, \beta)^{-1}, where I(\alpha, \beta) is the Fisher information matrix per observation: I(\alpha, \beta) = \begin{pmatrix} \psi'(\alpha) - \psi'(\alpha + \beta) & -\psi'(\alpha + \beta) \\ -\psi'(\alpha + \beta) & \psi'(\beta) - \psi'(\alpha + \beta) \end{pmatrix}. Here, \psi'(\cdot) is the trigamma function, the derivative of the digamma function. The inverse of this matrix provides the asymptotic variances and covariance, enabling approximate confidence intervals via \hat{\alpha} \pm z_{\alpha/2} \sqrt{ [I(\hat{\alpha}, \hat{\beta})^{-1}]_{11}/n } (and similarly for \hat{\beta}). This confirms the asymptotic efficiency of the MLE under regularity conditions. The MLE is consistent and efficient for large n, but practical challenges arise when sample values are near the boundaries 0 or 1, potentially leading to unstable or extreme estimates for \alpha or \beta (e.g., approaching 0), as the log-likelihood becomes flat near boundaries due to the open support of the distribution. In such cases, data truncation or alternative estimators may be considered to mitigate bias and variance inflation.

Bayesian inference

In Bayesian inference, the Beta distribution is widely used as a conjugate prior for estimating the success probability p in a binomial model, where the likelihood arises from observing s successes in n independent trials. This conjugacy, first formalized in the context of decision theory, ensures that the posterior distribution remains in the Beta family, facilitating analytical tractability without requiring numerical approximations. Specifically, with a prior p \sim \text{Beta}(\alpha, \beta), the posterior after observing the data is p \mid \text{data} \sim \text{Beta}(\alpha + s, \beta + n - s). The posterior mean provides a natural point estimate, given by \frac{\alpha + s}{\alpha + \beta + n}, which shrinks the maximum likelihood estimate s/n toward the prior mean \alpha/(\alpha + \beta) by an amount depending on the prior strength \alpha + \beta relative to the sample size n. Credible intervals, which quantify uncertainty in p, are readily computed as the \gamma-quantiles of this posterior Beta distribution, offering probabilistic bounds that incorporate both data and prior information. For prediction, the posterior predictive distribution for the number of successes in m future trials follows a Beta-binomial distribution, which accounts for the remaining uncertainty in p and typically exhibits overdispersion relative to a plain binomial. This predictive form integrates the posterior over p, yielding f(k \mid s, n, \alpha, \beta, m) = \binom{m}{k} \frac{B(\alpha + s + k, \beta + n - s + m - k)}{B(\alpha + s, \beta + n - s) B(\alpha, \beta)}, where B denotes the beta function, and k is the number of future successes. In hierarchical Bayesian models, the Beta distribution frequently serves as a hyperprior for parameters that represent proportions or rates, enabling partial pooling across groups to borrow strength from the data while regularizing estimates in sparse subgroups. For instance, in multi-group settings, group-specific probabilities p_i may each follow a Beta prior with hyperparameters \alpha and \beta drawn from a higher-level distribution, allowing the model to capture heterogeneity and shared structure simultaneously. This approach is particularly effective in applications involving clustered or exchangeable data, such as clinical trials or ecological sampling. The conjugacy of the Beta-binomial pair offers key advantages in modeling proportions, as it naturally accommodates uncertainty in p through interpretable prior pseudocounts \alpha and \beta, leading to robust inference even with limited data.

Choice of priors

In Bayesian analysis of the binomial distribution, the choice of prior for the success probability p is typically a Beta distribution due to conjugacy, allowing the posterior to remain Beta. Common noninformative priors include the uniform prior, the Haldane prior, and the Jeffreys prior, each with distinct properties affecting the posterior inference. The uniform prior, Beta(1,1), assumes no prior knowledge by placing equal density across [0,1]. With binomial data showing s successes in n trials, the posterior is Beta(1 + s, 1 + n - s), yielding a posterior mean of \frac{1 + s}{2 + n}. This corresponds to Laplace's rule of succession, which estimates the probability of the next success as \frac{s + 1}{n + 2}, providing a conservative adjustment even with limited or zero data. The Haldane prior, Beta(0,0), is improper as its density \pi(p) \propto p^{-1}(1-p)^{-1} integrates to infinity over [0,1]. It leads to a posterior Beta(s, n - s) with mean \frac{s}{n}, equivalent to the maximum likelihood estimate, but encounters issues when s = 0 or s = n, rendering the posterior improper and undefined at the boundaries. The Jeffreys prior, Beta(\frac{1}{2}, \frac{1}{2}), derives from the square root of the Fisher information, ensuring invariance under reparameterization such as the logit transform. The resulting posterior is Beta(\frac{1}{2} + s, \frac{1}{2} + n - s), with mean \frac{s + \frac{1}{2}}{n + 1}, offering a slight shrinkage toward 0.5 compared to the Haldane prior. The impact of these priors on the posterior varies with their strength: informative priors with small effective sample size (low \alpha + \beta) exert more shrinkage toward the prior mean, while vague priors with large \alpha + \beta (centered near the data) yield posteriors approaching the maximum likelihood estimate. Among noninformative options for the binomial, the Jeffreys prior minimizes expected posterior loss under squared error in certain decision-theoretic frameworks, whereas the uniform prior via Laplace's rule is particularly suited for scenarios with zero prior data, avoiding overconfidence in extremes. These choices influence posterior updates as detailed in the Bayesian inference section, balancing prior beliefs with observed evidence.

Applications

Order statistics and inference

In an independent and identically distributed (i.i.d.) sample of size n from the Uniform(0,1) distribution, the k-th order statistic U_{(k)} follows a Beta(k, n - k + 1) distribution. This result arises because the order statistics of uniform random variables can be represented using exponential spacings, leading to the Beta form through normalization. For a more general Uniform(a, b) distribution, the k-th order statistic is given by a + (b - a) U_{(k)}, where U_{(k)} retains the Beta(k, n - k + 1) distribution, providing a scaled version suitable for bounded intervals. This connection enables exact inference for quantiles in nonparametric settings. For instance, confidence intervals for an arbitrary p-quantile can be constructed using the Beta distribution of fractional order statistics, where the interval endpoints are determined by inverting the Beta cumulative distribution function to achieve the desired coverage probability. Specifically, for the median (p = 0.5), the coverage probability of an interval based on central order statistics, such as [U_{((n+1)/2)}, can be computed exactly using Beta probabilities, ensuring conservative or exact coverage without assuming an underlying parametric form. These methods outperform asymptotic approximations in small samples by leveraging the precise distributional properties of the Beta. In nonparametric contexts, the Beta distribution facilitates smoothing of the empirical cumulative distribution function (CDF). Bernstein polynomials, which expand the CDF as a mixture of Beta densities centered at order statistics, provide a nonparametric estimator that converges uniformly and allows for bias reduction in density estimation. Additionally, Beta-based approximations appear in bootstrap procedures for estimating proportions, where the variability of empirical proportions is modeled via Beta quantiles to construct confidence intervals, enhancing accuracy over naive resampling in finite samples. The properties of order statistics also extend to spacings, defined as the differences D_i = U_{(i)} - U_{(i-1)} (with U_{(0)} = 0 and U_{(n+1)} = 1). The vector of spacings follows a Dirichlet distribution, with marginal distributions that are Beta, such as the first spacing D_1 \sim \text{Beta}(1, n). These exact distributions for spacings are particularly useful in goodness-of-fit testing, where statistics based on ordered spacings detect deviations from uniformity or other hypothesized distributions by comparing observed gaps to their Beta-expected behavior.

Bayesian modeling

In Bayesian modeling, the Beta distribution serves as a natural prior for the success probability parameter in binomial or logistic regression setups, where the response variable represents binary outcomes such as success or failure. This choice leverages the Beta's support on the interval [0,1], aligning directly with probability parameters, and facilitates closed-form posterior updates when paired with binomial likelihoods. For instance, in logistic regression for binary classification tasks, a Beta prior on the underlying probability can be incorporated through the inverse logit transformation of linear predictors, enabling interpretable uncertainty quantification over predicted probabilities. Hierarchical Bayesian models often employ Beta distributions to model heterogeneity in success probabilities across groups or subgroups, resulting in Beta-binomial mixtures that account for overdispersion beyond standard binomial assumptions. In such frameworks, individual group probabilities p_i are drawn from a Beta hyperprior, allowing data from multiple units to "borrow strength" and shrink estimates toward a global mean, which is particularly useful in settings like ecological inference or meta-analysis of proportions. This structure promotes robust inference by incorporating variation at multiple levels, with the Beta's flexibility in capturing skewness and multimodality enhancing model fit for clustered data. The Beta distribution also plays a foundational role in nonparametric Bayesian approaches, such as Dirichlet process mixtures, where it underpins the stick-breaking construction introduced by Sethuraman. In this representation, a sequence of Beta(1, \alpha) random variables determines the weights for an infinite mixture of components, enabling flexible modeling of unknown numbers of clusters in density estimation or topic modeling without specifying a fixed dimensionality. This construction allows the Dirichlet process to generate discrete distributions with a countably infinite support, facilitating scalable posterior sampling in complex latent variable models. In practical applications like A/B testing for conversion rates, the Beta prior enables straightforward posterior updates based on observed successes and trials, providing probabilistic statements on the superiority of one variant over another. For example, starting with a weakly informative Beta prior, the posterior distribution yields credible intervals and probabilities of uplift, supporting decision-making under uncertainty in web experimentation. The interpretability of Beta parameters as pseudo-observations makes it intuitive for practitioners, while its conjugacy simplifies Markov chain Monte Carlo (MCMC) sampling in extended models, ensuring efficient computation even with hierarchical extensions.

Population genetics

In population genetics, the Beta distribution plays a central role in modeling allele frequencies under genetic drift in the Wright-Fisher model. In this discrete-generation model of a diploid population of effective size N_e, the stationary distribution of the frequency p of a neutral allele, incorporating symmetric mutation rates \mu from one allele to the other, is given by the Beta distribution with shape parameters $4N_e\mu and $4N_e\mu. Without mutation (\mu = 0), the process absorbs at the boundaries 0 and 1, with no stationary distribution in (0,1); fixation or loss occurs due to drift. The uniform Beta(1,1) distribution arises specifically when $4N_e \mu = 1. This arises from the diffusion approximation to the Wright-Fisher process, where the Beta captures the balance between drift, which pulls frequencies toward fixation or loss, and mutation, which maintains polymorphism. The mean allele frequency is 0.5 under symmetry, and the variance decreases with stronger mutation relative to drift, providing a foundational tool for predicting polymorphism levels. For multiple alleles, the multivariate extension in the Wright-Fisher model uses the Dirichlet distribution for the joint allele frequencies, with the marginal distribution for any two alleles following a Beta distribution. This setup is particularly useful in the Dirichlet-multinomial framework, where allele counts in a sample are modeled as multinomial draws conditional on underlying frequencies drawn from a Dirichlet prior, equivalent to a Beta prior for the two-allele case. Such models account for population substructure by treating allele frequencies across subpopulations as draws from a common Dirichlet, enabling inference on migration and differentiation while the Beta marginal facilitates pairwise analyses. In coalescent theory, which traces lineages backward in time, the Beta distribution serves as a conjugate prior for parameters bounded in [0,1], such as scaled mutation rates or selection coefficients in structured populations. For instance, Beta priors are applied to the probability of lineage migration or allele sharing in inference under the structured coalescent, facilitating Bayesian estimation of demographic parameters from sequence data. This prior choice leverages the Beta's flexibility in modeling proportions, ensuring posterior updates remain tractable within the coalescent framework. The Beta distribution also informs likelihood-based estimation from allele frequency spectra in high-throughput sequencing data, where observed variant counts approximate a Beta-binomial process under the Wright-Fisher diffusion. Methods augment the Beta with point masses at boundaries (Beta-with-spikes) to better fit spectra including fixed or lost alleles, enabling accurate inference of N_e, mutation rates, and divergence times from site frequency data. Historically, the Beta distribution's application traces to early models of polymorphism, including extensions of the infinite alleles model where it parameterizes the prior on root allele frequencies to compute expected heterozygosity across loci. In this context, heterozygosity H = 1 - \sum p_i^2, with frequencies integrated over a Beta prior, yields predictions matching observed neutral diversity under mutation-drift balance.

Project management

In project management, the Beta distribution is prominently used in the Program Evaluation and Review Technique (PERT) to model uncertainty in task durations and costs, where estimates are typically provided as three expert judgments: the optimistic minimum a, the most likely mode m, and the pessimistic maximum b. This setup defines a four-parameter generalization of the standard Beta distribution, scaled to the interval [a, b], with shape parameters \alpha and \beta fitted to reflect the emphasis on the mode. The parameters are calculated as \alpha = 1 + 4\frac{m - a}{b - a} and \beta = 1 + 4\frac{b - m}{b - a}, ensuring the distribution assigns greater probability density around m. The mean of this PERT distribution is given by \mu = a + (b - a) \frac{\alpha}{\alpha + \beta} = \frac{a + 4m + b}{6}, which weights the mode four times more than the extremes, while the variance is \sigma^2 = \frac{(b - a)^2}{36}, capturing the scaled spread of the Beta distribution. These properties enable integration with the critical path method, where task durations along the project's longest sequence are simulated via Monte Carlo methods to estimate overall completion probabilities and identify risks. For instance, in large-scale projects like the Polaris missile program, PERT Beta modeling helped quantify schedule uncertainties by propagating task variances through network dependencies. The approach offers advantages in accounting for estimation uncertainty through its bounded support and asymmetric shapes, which naturally accommodate optimistic and pessimistic views without allowing unbounded tails, and it remains endorsed in standards like the Project Management Body of Knowledge. Limitations include the assumption of fixed minima and maxima, which may not reflect real-world flexibility, and the challenge of fitting four parameters from only three estimates, leading modern practices to favor Monte Carlo simulations with more adaptable distributions for enhanced accuracy.

Other fields

In machine learning, the Beta distribution serves as a conjugate prior in variational inference frameworks for topic models, where it underpins the Dirichlet distribution used in Latent Dirichlet Allocation (LDA) to model document-topic proportions as bounded probabilities between 0 and 1. Similarly, Beta priors are employed in Gaussian processes to handle proportion outcomes, enabling flexible modeling of uncertainty in predictive distributions for rates or fractions, such as success probabilities in classification tasks. This arises from the Beta's ability to capture skewed or U-shaped densities suitable for hyperparameters in non-linear regression settings. In wavelet analysis, the Beta distribution models the sparsity of wavelet coefficients for signal denoising, particularly for bounded-energy signals, by using symmetric Beta priors around zero to shrink insignificant coefficients while preserving signal structure. This approach enhances Bayesian wavelet shrinkage estimators, promoting adaptive thresholding that balances noise reduction and feature retention in multi-resolution decompositions. Subjective logic leverages the Beta distribution to represent belief masses in opinions, treating them as parameters of a Beta probability density function to quantify uncertainty in evidence for binomial propositions. In opinion pooling, this enables algebraic combination of subjective beliefs from multiple sources, where the uncertainty component (vacuity) reflects evidential gaps, facilitating robust fusion in uncertain environments like decision support systems. For instance, binomial opinions map directly to Beta distributions, allowing projective and commutative operations for aggregating distributed trust assessments. In economics, the Beta distribution parameterizes Lorenz curves to model income inequality, deriving distribution parameters from empirical Lorenz data to estimate Gini coefficients and inequality indices with high flexibility for various skewness levels. Generalized Beta variants extend this to capture heavy tails in income distributions, enabling parametric comparisons across populations via closed-form Lorenz expressions. Additionally, in choice models, Beta distributions model heterogeneous preferences over bounded choice probabilities, supporting Bayesian inference on utility parameters in discrete choice settings with unobserved alternatives. Post-2020 applications include AI ethics, where Beta distributions model fairness probabilities in algorithmic bias mitigation, using them as priors for proportion-based metrics like demographic parity to quantify and constrain disparities in predictive outcomes. In COVID-19 modeling, Beta regression analyzes transmission rates as bounded proportions, fitting daily incidence and mortality data to predict epidemic trajectories and evaluate intervention impacts across regions. For example, Beta-based models have estimated country-specific reproduction numbers by regressing confirmed cases against covariates like mobility, highlighting the distribution's suitability for overdispersed count data normalized to [0,1]. The Beta distribution's versatility stems from its support on [0,1], making it ideal for modeling rates, proportions, and probabilities in bounded domains across these fields, often in conjunction with extensions like the Beta-binomial for overdispersion in machine learning applications.

Computation

Random variate generation

One common method for generating random variates from the Beta(\alpha, \beta) distribution exploits its relationship to the Gamma distribution. Specifically, if Y_1 \sim \text{Gamma}(\alpha, 1) and Y_2 \sim \text{Gamma}(\beta, 1) are independent, then X = Y_1 / (Y_1 + Y_2) follows Beta(\alpha, \beta). This approach is exact provided that Gamma variates can be generated accurately, though it becomes inefficient when \alpha and \beta differ substantially due to the need for multiple Gamma samples per Beta variate. The inversion method provides another exact technique by solving F(x) = U for x, where F is the cumulative distribution function (CDF) of the Beta distribution and U \sim \text{Uniform}(0,1). This requires computing the inverse of the regularized incomplete beta function I_x(\alpha, \beta), which can be evaluated using continued fraction expansions or asymptotic approximations for efficiency. Algorithms such as those based on bisection combined with continued fractions achieve high precision, with implementations ensuring relative errors below $10^{-15} for typical parameter ranges. Special cases simplify generation further. When \alpha = \beta = 1, the Beta distribution reduces to Uniform(0,1), so variates are directly sampled from the uniform distribution. For integer-valued \alpha, \beta \geq 1, Jöhnk's rejection sampling algorithm is particularly efficient: it generates a candidate u \sim \text{Uniform}(0,1) and an integer k \sim \text{Binomial}(\alpha + \beta - 1, u), accepting x = k / (\alpha + \beta - 1) with probability proportional to a polynomial envelope, yielding high acceptance rates (often exceeding 90%) for moderate integers. Advanced algorithms enhance speed for general parameters. The Ziggurat method, adapted for the Beta density via transformations, uses stacked rectangular regions under the density curve for rapid acceptance-rejection, achieving generation times comparable to uniform sampling on modern hardware. For log-concave cases (\alpha, \beta > 1), adaptive rejection sampling (ARS) builds piecewise exponential envelopes iteratively from initial evaluations of the log-density, attaining near-100% acceptance after adaptation while handling non-integer parameters robustly. Statistical software libraries implement hybrid strategies combining these methods. In R, the rbeta function switches between the Gamma ratio for \min(\alpha, \beta) > 1, Jöhnk's method for integers, and Cheng's rejection sampler otherwise, ensuring efficient performance across parameter spaces. Similarly, Python's SciPy beta.rvs employs analogous algorithms, including inversion via the inverse incomplete beta for edge cases.

Approximations

For large values of the shape parameters \alpha and \beta, the Beta distribution \operatorname{Beta}(\alpha, \beta) can be approximated by a normal distribution with mean \mu = \frac{\alpha}{\alpha + \beta} and variance \sigma^2 = \frac{\alpha \beta}{(\alpha + \beta)^2 (\alpha + \beta + 1)}. This approximation arises from the central limit theorem applied to the representation of the Beta as a ratio of independent Gamma random variables, scaled appropriately. In the symmetric case where \alpha = \beta = b, the standardized transformation Y = 2\sqrt{2b + 1}(X - 1/2) of a \operatorname{Beta}(b, b) random variable X converges in distribution to the standard normal as b \to \infty. The normal approximation performs well in the central region of the distribution but is less accurate near the boundaries 0 and 1, where the Beta density may exhibit significant mass or sharp behavior while the normal extends to unbounded values. Continuity corrections, such as adjusting the bounds by small increments like 0.5 scaled to the support, can mitigate boundary effects in probability calculations, though they are more commonly applied in discrete-to-continuous approximations. To address boundary issues more effectively, the logit transformation \log\left(\frac{X}{1 - X}\right) of a Beta random variable yields an approximately normal distribution for large \alpha and \beta, leveraging the bounded support. The Johnson SB (bounded) system offers a flexible four-parameter family for approximating distributions on a finite interval, such as the Beta, through a logarithmic transformation of the normal distribution. Specifically, a random variable Y follows a Johnson SB distribution if Z = \gamma + \delta \log\left(\frac{Y - \epsilon}{\lambda - (Y - \epsilon)}\right) is standard normal, where \epsilon and \lambda > 0 define the bounds, and \gamma, \delta > 0 control location and shape; this form closely fits the Beta's bounded support and variable skewness. Introduced by Johnson in 1949, the system was designed to translate non-normal data to normality while covering a wide range of shapes encountered in bounded empirical distributions. The Edgeworth expansion refines the normal approximation to the Beta distribution by incorporating higher cumulants, such as skewness \frac{2(\beta - \alpha)\sqrt{\alpha + \beta + 1}}{(\alpha + \beta + 2)\sqrt{\alpha \beta}} and excess kurtosis \frac{6\left[(\alpha - \beta)^2 (\alpha + \beta + 1) - \alpha \beta (\alpha + \beta + 2)\right]}{\alpha \beta (\alpha + \beta + 2)(\alpha + \beta + 3)}, to capture asymmetries and tail behaviors more accurately. The expansion expresses the density as a series: f(x) \approx \phi(z) \left[1 + \frac{\kappa_3}{6\sqrt{n}} H_3(z) + \frac{\kappa_4}{24 n} H_4(z) + \cdots \right], where \phi is the standard normal density, z = (x - \mu)/\sigma, H_k are Hermite polynomials, and \kappa_j are standardized cumulants; this provides improved tail probabilities beyond the basic normal fit. The Laplace approximation facilitates computation of integrals involving the Beta distribution, particularly in Bayesian settings for posterior modes or expectations when the Beta serves as a prior or likelihood component in non-conjugate models. For an integral \int g(\theta) e^{n h(\theta)} d\theta, it approximates around the mode \hat{\theta} by a Gaussian with mean \hat{\theta} and variance [-n h''(\hat{\theta})]^{-1}, yielding \int \approx g(\hat{\theta}) e^{n h(\hat{\theta})} \sqrt{2\pi / [-n h''(\hat{\theta})]}; in Beta-related posteriors, such as Beta-binomial extensions, this captures the mode and curvature effectively for large samples. For the Beta itself, the approximation aligns exactly with certain moment-generating function methods under logit reparameterization, enhancing accuracy for transformed expectations.

History

The mathematical foundations of the beta function trace back to a letter Isaac Newton wrote to Henry Oldenburg on October 24, 1676, in which he provided a series expansion for an integral form related to the incomplete beta function. Leonhard Euler developed the beta integral, now known as the beta function, in the early 18th century; it first appeared in his correspondence around 1729 and was formally presented in his 1748 work Introductio in analysin infinitorum. The name "beta function" was later given by Jacques Binet in 1839, in reference to the Greek letter Β. The beta distribution itself emerged in probabilistic contexts with Thomas Bayes, who in an essay published posthumously in 1763 used it (without naming it) as a conjugate prior for the binomial distribution, marking one of the earliest applications in Bayesian inference. In the late 19th century, Karl Pearson incorporated the beta distribution into his system of frequency curves, classifying it as Type I in 1895 and providing extensive tables and applications in 1922. This work helped establish its role in statistical modeling of bounded variables.