Fact-checked by Grok 2 weeks ago

Euler integral

In mathematics, the Euler integrals comprise two pivotal special functions introduced by the Swiss mathematician Leonhard Euler in the early 18th century: the Euler integral of the first kind, which defines the beta function as B(x, y) = \int_0^1 t^{x-1} (1-t)^{y-1} \, dt for real parts \Re(x) > 0 and \Re(y) > 0, and the Euler integral of the second kind, which defines the gamma function as \Gamma(z) = \int_0^\infty t^{z-1} e^{-t} \, dt for \Re(z) > 0.^[1]^[2] These integrals arose from Euler's efforts to extend the factorial function to non-integer values, with the gamma function providing an analytic continuation where \Gamma(n+1) = n! for positive integers n.^[1] Euler first proposed the gamma integral representation in a 1729 letter to Christian Goldbach, motivated by interpolating sequences like factorials amid challenges in evaluating infinite products.^[3] The beta function, symmetric in its arguments (B(x, y) = B(y, x)), connects directly to the gamma function via the identity B(x, y) = \frac{\Gamma(x) \Gamma(y)}{\Gamma(x+y)}, enabling evaluations of definite integrals across probability distributions, physics, and engineering.^[2]^[1] Key properties of the gamma function include the functional equation \Gamma(z+1) = z \Gamma(z), which underpins its recursive nature, and the reflection formula \Gamma(z) \Gamma(1-z) = \frac{\pi}{\sin(\pi z)}, useful for computations near poles.^[2] For half-integers, \Gamma\left(\frac{1}{2}\right) = \sqrt{\pi}, linking it to Gaussian integrals and error functions.^[2] The beta function similarly facilitates integral transforms and appears in the normalization of the beta distribution in statistics.^[1] Together, these Euler integrals form foundational tools in complex analysis, special functions theory, and applied mathematics, with extensions to fractional calculus and beyond.^[2]

Definitions and Notation

Integral of the First Kind

The Euler integral of the first kind, also known as the beta function, is defined by the integral representation

B(x,y) = \int_0^1 t^{x-1} (1-t)^{y-1} \, dt,

where the parameters x and y are complex numbers satisfying \Re(x) > 0 and \Re(y) > 0.^[4] This integral is improper at the endpoints t=0 and t=1, as the integrand t^{x-1} (1-t)^{y-1} may diverge there when \Re(x) \leq 1 or \Re(y) \leq 1. Near t=0, the behavior is dominated by t^{x-1}, which is integrable provided \Re(x) > 0; similarly, near t=1, the term (1-t)^{y-1} ensures integrability if \Re(y) > 0. These conditions guarantee the convergence and finiteness of the integral over the finite interval [0,1].^[4] The standard notation B(x,y) emphasizes the symmetry in the parameters, with the restrictions \Re(x) > 0 and \Re(y) > 0 imposed to ensure the integral converges absolutely. For values outside these regions, the function can be defined via analytic continuation, but the integral form holds only within the specified domain.^[5] The Euler integral of the first kind is closely related to the gamma function through the identity B(x,y) = \Gamma(x) \Gamma(y) / \Gamma(x+y), with details explored in subsequent sections.^[4]

Integral of the Second Kind

The Euler integral of the second kind provides the standard integral representation of the Gamma function, defined as

\Gamma(z) = \int_0^\infty t^{z-1} e^{-t} \, dt

for complex numbers z with positive real part, \Re(z) > 0.^[6] This representation extends the factorial to non-integer values, satisfying \Gamma(n+1) = n! for non-negative integers n.^[7] This form can be obtained from the finite integral \int_0^1 (-\ln x)^{z-1} \, dx = \Gamma(z) via the substitution t = -\ln x (or equivalently, x = e^{-t}, with dx = -e^{-t} \, dt). As x ranges from 0 to 1, t ranges from \infty to 0, and flipping the limits yields the semi-infinite integral.^[1]^[2] The integral converges absolutely for \Re(z) > 0, as the integrand t^{z-1} e^{-t} remains integrable over the semi-infinite interval.^[6] Near t = 0, the behavior is dominated by t^{\Re(z)-1}, which is integrable provided \Re(z) > 0; as t \to \infty, the polynomial growth of t^{z-1} is overwhelmed by the rapid exponential decay of e^{-t}, ensuring the tail of the integral vanishes.^[8] This exponential factor e^{-t} is crucial for integrability over the unbounded domain, enabling the representation's validity and facilitating analytic continuation beyond \Re(z) > 0.^[6]

Historical Development

Euler's Original Work

Daniel Bernoulli proposed an interpolation for the factorial function in a letter to Christian Goldbach dated October 6, 1729. Independently, Leonhard Euler initiated his investigations into generalizing the factorial function in a letter to Christian Goldbach dated October 13, 1729, where he proposed an infinite product formula to interpolate n! for non-integer arguments. This work arose from Euler's broader efforts to extend discrete factorial values using continuous methods, motivated by problems in infinite series and the need for a general term in transcendental progressions. Euler expressed the generalized factorial, denoted as what would later become Γ(z+1), through a limit involving rising factorials:

\Gamma(z+1) = \lim_{n \to \infty} \frac{1 \cdot 2 \cdots n \cdot n^z}{(z+1)(z+2) \cdots (z+n)}.

This representation allowed Euler to compute values for fractional arguments, such as half-integers, by relating the product to the infinite product expansion of the sine function derived from earlier work on series.^[9]^[10]^[11] In a follow-up letter to Goldbach on January 8, 1730, and in his contemporaneous paper "De progressionibus transcendentibus, seu quarum termini generales algebraice dari nequeunt" presented to the St. Petersburg Academy, Euler introduced an integral representation to evaluate these generalized factorials. He employed what is now recognized as a Beta-like integral of the form \int_0^1 t^{z-1} (1-t)^{n-1} \, dt for integer n, using it to derive specific values and connect to infinite products. For instance, Euler computed the value for z = 1/2 by linking the product to the sine expansion \frac{\sin \pi x}{\pi x} = \prod_{k=1}^\infty \left(1 - \frac{x^2}{k^2}\right), yielding \Gamma(1/2) = \sqrt{\pi} after squaring and taking limits, a result that established an important link between the interpolated factorial and trigonometric functions.^[12] Euler further explored these integrals in the context of series expansions in his 1748 treatise Introductio in analysin infinitorum, where Beta-like forms appeared in the development of binomial expansions for non-integer exponents, such as (1 + x)^a = \sum_{k=0}^\infty \binom{a}{k} x^k, with coefficients involving integrals over powers. This built directly on his earlier interpolation efforts, emphasizing the role of such integrals in representing transcendental functions through infinite series. These contributions underscored Euler's innovative use of integrals and products to bridge discrete and continuous mathematics, providing foundational tools for later special function theory.^[13]^[14]

Evolution and Modern Usage

In the early 19th century, Adrien-Marie Legendre formalized the notation for Euler's integral of the second kind by introducing the symbol \Gamma(z) in his 1811 treatise Exercices de calcul intégral, establishing a standard representation for the function extending the factorial to real and complex arguments.^[8] Concurrently, Carl Friedrich Gauss contributed to its study in 1813 by adopting the notation \Pi(z) for \Gamma(z+1) while investigating the hypergeometric series, and he provided rigorous proofs of key properties including the multiplication formula.^[15] Legendre also examined the integral of the first kind, which was later designated the Beta function B(x,y), with the symbol B coined by Jacques Binet in 1839 to denote this pairwise product form related to the Gamma function.^[5] In 1859, Bernhard Riemann extended the domain of Euler's integrals beyond their initial positive real parameter restrictions through a contour integral representation of the reciprocal Gamma function, facilitating analytic continuation across the complex plane while accounting for poles at non-positive integers.^[10] During the 20th century, Euler integrals became integral to complex analysis curricula, as exemplified in E. T. Whittaker and G. N. Watson's 1927 textbook A Course of Modern Analysis, which treats them as essential tools for deriving properties of special functions like the hypergeometric series.^[16] In contemporary special functions literature, "Euler integral" specifically denotes the defining integral forms of the Beta and Gamma functions, with the Gamma representation \Gamma(z) = \int_0^\infty e^{-t} t^{z-1} \, dt for \operatorname{Re}(z) > 0 serving as a foundational expression in authoritative compilations such as the NIST Digital Library of Mathematical Functions (as of 2025).^[6]

Mathematical Properties

Analytic Continuation and Convergence

The Euler integral of the first kind, defined as B(x, y) = \int_0^1 t^{x-1} (1-t)^{y-1} \, dt, converges absolutely for \Re(x) > 0 and \Re(y) > 0. Similarly, the Euler integral of the second kind, given by \Gamma(z) = \int_0^\infty t^{z-1} e^{-t} \, dt, converges absolutely for \Re(z) > 0. An alternative representation of the beta function as an improper integral, B(x, y) = \int_0^\infty \frac{t^{x-1}}{(1+t)^{x+y}} \, dt, also requires \Re(x) > 0 and \Re(y) > 0 for absolute convergence. Beyond these half-planes, the functions admit meromorphic continuations to the entire complex plane. The gamma function, underlying both Euler integrals through the relation B(x, y) = \frac{\Gamma(x) \Gamma(y)}{\Gamma(x+y)}, is extended meromorphically via its functional equation and other representations. A key tool for this continuation is the reflection formula \Gamma(z) \Gamma(1 - z) = \frac{\pi}{\sin(\pi z)}, valid for z \notin \{0, \pm 1, \pm 2, \dots \}, which allows extension across the principal strip and reveals the meromorphic structure. The gamma function has simple poles at the non-positive integers z = 0, -1, -2, \dots, with residues given by \operatorname{Res}(\Gamma, -n) = \frac{(-1)^n}{n!} for nonnegative integers n. Consequently, the beta function inherits poles where x or y is a non-positive integer but x + y is not, and residues at these points can be computed using the residues of the constituent gamma functions, for example, \operatorname{Res}(B(x, y), x = -n) = \frac{(-1)^n}{n!} \frac{\Gamma(y)}{\Gamma(y - n)} when y - n avoids poles. For large |z| with |\arg z| < \pi - \delta and fixed \delta > 0, the asymptotic behavior of the gamma function is captured by Stirling's approximation:

\Gamma(z) \sim \sqrt{2\pi / z} \, (z/e)^z \left( 1 + \frac{1}{12z} + \frac{1}{288z^2} - \cdots \right),

where the series is an asymptotic expansion in powers of $1/z. This provides the leading-order growth \Gamma(z) \sim \sqrt{2\pi / z} \, (z/e)^z and enables evaluation in regions far from the poles.

Functional Equations and Identities

The Euler integrals satisfy several fundamental functional equations that underpin their algebraic structure and facilitate computations across complex analysis and special functions. For the integral of the second kind, which is intimately connected to the gamma function \Gamma(z), a key recurrence relation holds: \Gamma(z+1) = z \Gamma(z) for \Re(z) > 0. This relation, derived through integration by parts on the defining integral representation, extends the factorial property to non-integer arguments and enables iterative evaluation of \Gamma(z) at successive points. It forms the basis for many recursive algorithms in numerical analysis of special functions.^[6] A more advanced identity is the multiplication theorem, also known as Gauss's multiplication formula, which generalizes the duplication formula for the gamma function. For a positive integer n, it states:

\Gamma(nz) = (2\pi)^{(n-1)/2} n^{nz - 1/2} \prod_{k=0}^{n-1} \Gamma\left(z + \frac{k}{n}\right),

valid for \Re(z) > 0. This theorem, first rigorously established by Carl Friedrich Gauss in his investigation of infinite series, reveals multiplicative symmetries in the gamma function and is crucial for deriving product representations and approximations in asymptotic analysis. It has widespread applications in evaluating gamma values at rational multiples and in the theory of elliptic integrals.^[17] Turning to the integral of the first kind, the beta function B(x,y) exhibits a simple yet profound symmetry: B(x,y) = B(y,x) for \Re(x) > 0, \Re(y) > 0. This symmetry arises directly from the interchangeable roles of the parameters in the defining integral and the relation B(x,y) = \Gamma(x)\Gamma(y)/\Gamma(x+y), allowing straightforward swaps in identities and expansions. It proves particularly useful in partial fraction decompositions of rational functions involving products of linear terms, where the beta function provides integral representations that simplify the coefficients of the decomposition, as seen in evaluations of sums like \sum 1/(k(k+1)\cdots(k+m)).^[4] An important derivative identity emerges from the logarithmic differentiation of the gamma function associated with the second kind: the digamma function \psi(z) is defined as \psi(z) = \frac{d}{dz} \ln \Gamma(z) = \frac{\Gamma'(z)}{\Gamma(z)} for \Re(z) > 0. This function captures the rate of change of \ln \Gamma(z) and satisfies its own recurrence \psi(z+1) = \psi(z) + 1/z, linking it closely to the primary gamma recurrence. Introduced in the context of polygamma functions, \psi(z) is essential for studying the asymptotic behavior of gamma ratios and appears in harmonic number generalizations, such as \psi(n+1) = -\gamma + H_n for positive integers n, where \gamma is the Euler-Mascheroni constant.^[6]

Relations to Special Functions

Connection to the Beta Function

The Euler integral of the first kind is defined as the Beta function B(x,y) = \int_0^1 t^{x-1} (1-t)^{y-1} \, dt for \operatorname{Re}(x) > 0 and \operatorname{Re}(y) > 0.^[5] This integral representation establishes the Beta function as a fundamental special function with deep ties to other mathematical structures. A key identity links the Beta function to the Gamma function:

B(x,y) = \frac{\Gamma(x) \Gamma(y)}{\Gamma(x+y)},

which transforms the Euler integral of the first kind into a product involving integrals of the second kind, facilitating analytic continuations and evaluations across complex domains. For positive integers m and n, the Beta function provides an explicit expression for binomial-related quantities:

B(m+1, n+1) = \frac{m! \, n!}{(m+n+1)!},

offering an integral form that connects combinatorial coefficients to continuous analysis.^[5] The Beta function features prominently in the integral representation of the Gauss hypergeometric function:

{}_2F_1(a,b;c;z) = \frac{\Gamma(c)}{\Gamma(b) \Gamma(c-b)} \int_0^1 t^{b-1} (1-t)^{c-b-1} (1 - z t)^{-a} \, dt,

for |z| < 1, Re(c) > Re(b) > 0, where the normalizing prefactor is the reciprocal of B(b, c-b), enabling series expansions and evaluations of hypergeometric functions. In probability, the Beta function serves as the normalizing constant for the Beta distribution, while its multivariate generalization normalizes the Dirichlet distribution over the simplex.^[18]

Connection to the Gamma Function

The Euler integral of the second kind serves as the defining integral representation for the Gamma function, expressed as

\Gamma(z) = \int_0^\infty t^{z-1} e^{-t} \, dt

for \Re(z) > 0. This improper integral converges absolutely in the right half-plane and yields an analytic function that interpolates the factorial for positive integers, where \Gamma(n+1) = n! for each non-negative integer n. The relation \Gamma(n+1) = n! establishes the Gamma function as a continuous extension of the factorial, bridging discrete combinatorics with complex analysis. An alternative representation, the Weierstrass infinite product, provides insight into the global structure of the Gamma function through its reciprocal:

\frac{1}{\Gamma(z)} = z e^{\gamma z} \prod_{k=1}^\infty \left(1 + \frac{z}{k}\right) e^{-z/k},

where \gamma \approx 0.57721 is the Euler-Mascheroni constant. This product form, valid for all complex z, underscores that $1/\Gamma(z) is an entire function with simple zeros at the non-positive integers, facilitating derivations of functional equations and asymptotic behaviors.^[19] To extend the definition beyond the convergence region of the Euler integral and navigate the poles at non-positive integers, the Hankel contour integral is utilized for the reciprocal:

\frac{1}{\Gamma(z)} = \frac{1}{2\pi i} \oint_H e^{t} t^{-z} \, dt,

where the contour H proceeds from -\infty along the real axis, encircles the origin in the positive direction while avoiding the branch cut along the negative real axis, and returns to -\infty. This representation holds for all z \in \mathbb{C} except the poles and enables numerical evaluation and analytic continuation in left half-planes. The power t^{-z} uses its principal value where the contour crosses the positive real axis.^[20] The Gamma function's integral form also underpins its prominence in transform theory. It coincides with the Mellin transform of the exponential decay e^{-t}:

\int_0^\infty t^{s-1} e^{-t} \, dt = \Gamma(s), \quad \Re(s) > 0.

^[21] Likewise, the Laplace transform of t^{z-1} yields \Gamma(z) s^{-z} for \Re(s) > 0 and \Re(z) > 0, linking the Gamma function to solutions of differential equations and probability densities./07:_Special_Functions/7.03:_Gamma_Function)

Generalizations and Extensions

Multivariate Euler Integrals

The multivariate Euler integral generalizes the classical Euler beta integral to higher dimensions, primarily through the Dirichlet integral, which evaluates over the standard simplex. The Dirichlet integral is defined as

\int_{S_K} \prod_{i=1}^K y_i^{\alpha_i - 1} \, d^{K-1}y = \frac{\prod_{i=1}^K \Gamma(\alpha_i)}{\Gamma\left( \sum_{i=1}^K \alpha_i \right)},

where S_K = \{ y = (y_1, \dots, y_K) \in \mathbb{R}^K : \sum_{i=1}^K y_i = 1, \, y_i \geq 0 \} is the (K-1)-dimensional simplex, and the integral is with respect to the induced Lebesgue measure on the simplex.^[22] This represents the multivariate beta function, extending the bivariate beta function B(\alpha, \beta) = \int_0^1 t^{\alpha-1} (1-t)^{\beta-1} \, dt = \frac{\Gamma(\alpha) \Gamma(\beta)}{\Gamma(\alpha + \beta)} by incorporating multiple power-law terms constrained to the simplex.^[22] The formula arises from iterative applications of the one-dimensional beta integral or via gamma function identities, providing a normalization constant in multivariate contexts.^[22] A further generalization is the Selberg integral, which introduces interaction terms between variables, making it a multivariate analogue of the beta integral with additional pairwise dependencies. It is given by

S_n(\alpha, \beta, \gamma) = \int_0^1 \cdots \int_0^1 \prod_{i=1}^n t_i^{\alpha-1} (1 - t_i)^{\beta-1} \prod_{1 \leq i < j \leq n} |t_i - t_j|^{2\gamma} \, dt_1 \cdots dt_n = \prod_{j=0}^{n-1} \frac{\Gamma(\alpha + j \gamma) \Gamma(\beta + j \gamma) \Gamma(1 + (j+1) \gamma)}{\Gamma(\alpha + \beta + (n + j - 1) \gamma) \Gamma(1 + \gamma)},

originally introduced by Atle Selberg in 1944.^[23] For n=1, it reduces precisely to the Euler beta integral B(\alpha, \beta).^[23] This integral has proven influential in random matrix theory and orthogonal polynomial ensembles, where the Vandermonde-like determinant \prod |t_i - t_j|^{2\gamma} captures repulsive interactions among variables.^[23] Lauricella functions extend these integrals to hypergeometric series in multiple variables, featuring Euler-type integral representations that generalize the beta integral's structure. For instance, the Lauricella function F_C^{(m)}(a, b; c_1, \dots, c_m; x_1, \dots, x_m) admits a representation involving a multidimensional integral over twisted cycles:

F_C(a, b, c; x) = \frac{\Gamma(1 - a)}{\prod_k \Gamma(1 - c_k) \cdot \Gamma\left( \sum_k c_k - a - m + 1 \right)} \int_\Delta \prod_k t_k^{-c_k} (1 - \sum_k t_k)^{\sum c_k - a - m} (1 - \sum_k x_k t_k)^{-b} \, dt_1 \wedge \cdots \wedge dt_m,

where \Delta is a suitable twisted cycle in the configuration space.^[24] This form connects to Euler integrals through the power weights and gamma function prefactors, enabling evaluations of multivariate hypergeometric sums in physics and geometry.^[24] The functions arise in solutions to partial differential equations with multiple singular points, broadening the scope of Euler integral applications.^[24] Convergence of these multivariate integrals requires stricter conditions than the univariate case due to the higher-dimensional geometry. For the Dirichlet integral, the integral converges absolutely when \operatorname{Re}(\alpha_i) > 0 for all i = 1, \dots, K, ensuring integrability near the boundaries and vertices of the simplex.^[22] The Selberg integral converges for \operatorname{Re}(\alpha) > 0, \operatorname{Re}(\beta) > 0, and \operatorname{Re}(\gamma) > -\min\{1/n, \operatorname{Re}(\alpha)/(n-1), \operatorname{Re}(\beta)/(n-1)\}, where the lower bound on \gamma prevents singularities from the pairwise terms overpowering the domain.^[23] For Lauricella functions, convergence holds in domains such as \{ (x_1, \dots, x_m) \in \mathbb{C}^m : \sum_k \sqrt{|x_k|} < 1 \}, with additional parameter restrictions like c_k, a - \sum c_k \notin \mathbb{Z} to avoid poles in the gamma functions.^[24] These conditions facilitate analytic continuation to larger regions, mirroring univariate behaviors but adapted to multi-variable interactions.^[24]

q-Analogues and Discrete Variants

The q-analogue of the gamma function, denoted \Gamma_q(z), is defined for $0 < q < 1 and complex z with positive real part by the infinite product

\Gamma_q(z) = (1-q)^{1-z} \prod_{k=0}^\infty \frac{1 - q^{k+1}}{1 - q^{k+z}}.

This definition, introduced by Thomae in 1869 and further developed by Jackson in 1910, serves as a deformation of Euler's gamma function that preserves key properties such as the functional equation \Gamma_q(z+1) = \frac{1 - q^z}{1 - q} \Gamma_q(z). As q \to 1, \Gamma_q(z) recovers the classical gamma function \Gamma(z). A corresponding q-analogue of the beta function is defined via B_q(a,b) = \Gamma_q(a) \Gamma_q(b) / \Gamma_q(a+b). This function admits integral representations, such as those using Jackson q-integrals, and connects directly to basic hypergeometric series through expansions involving q-Pochhammer symbols and the q-binomial theorem.^[25] More general q-beta integrals appear in the Askey-Wilson framework, which provides orthogonality measures for the Askey-Wilson polynomials—the most general family in the q-Askey scheme of basic hypergeometric orthogonal polynomials. The seminal Askey-Wilson integral is the contour integral

\frac{(q;q)_\infty}{2\pi i} \oint \frac{(z^2;q)_\infty (z^{-2};q)_\infty}{(z;q)_\infty (z^{-1};q)_\infty} \prod_{j=1}^4 \frac{(a_j z;q)_\infty (a_j^{-1} z;q)_\infty}{(z;q)_\infty (z^{-1};q)_\infty} \frac{dz}{z} = \prod_{1 \leq i < j \leq 4} (a_i a_j;q)_\infty,

over a suitable contour enclosing the origin, for parameters |q| < 1 and |a_j| < 1.^[26] This integral, derived in 1985, not only establishes the orthogonality of Askey-Wilson polynomials with respect to a weight function on the unit circle but also generalizes earlier q-integrals, linking to representations of very-well-poised basic hypergeometric series like {}_8\phi_7.^[26] In partition theory, q-analogues of Euler integrals arise through generating functions built from q-Pochhammer symbols, which encode partition statistics. For instance, the reciprocal of the Euler function (q;q)_\infty^{-1} = \sum_{n=0}^\infty p(n) q^n, where p(n) is the partition function, appears in the product form of \Gamma_q(z), enabling q-deformations of generating functions for restricted partitions, such as those by largest part or Durfee square size. These connections facilitate proofs of partition identities via q-integral evaluations, as explored in the theory of basic hypergeometric series.

Applications

In Probability and Statistics

In probability theory, the Beta distribution, defined on the interval (0,1), plays a fundamental role in modeling proportions and probabilities, with its probability density function given by f(t \mid \alpha, \beta) = \frac{1}{B(\alpha, \beta)} t^{\alpha-1} (1-t)^{\beta-1}, \quad 0 < t < 1, where \alpha > 0 and \beta > 0 are shape parameters, and B(\alpha, \beta) is the Beta function acting as the normalizing constant to ensure the density integrates to 1.^[27] This Euler integral form of the normalizing constant directly ties the distribution to the properties of the Beta function, enabling its use in Bayesian updating for binomial data where it serves as a conjugate prior.^[28] The Gamma distribution, with shape-rate parametrization, is another key application in statistical inference, featuring the probability density function f(x \mid \alpha, \beta) = \frac{\beta^\alpha}{\Gamma(\alpha)} x^{\alpha-1} e^{-\beta x}, \quad x > 0, where \alpha > 0 is the shape and \beta > 0 is the rate.^[29] In Bayesian statistics, this distribution is conjugate to the Poisson likelihood for the rate parameter \lambda, meaning the posterior distribution remains Gamma after observing Poisson data, with updated parameters \alpha' = \alpha + \sum x_i and \beta' = \beta + n, where n is the number of observations and \sum x_i is their total.^[30] This conjugacy simplifies posterior computations and is widely used in modeling count data, such as event rates in reliability analysis or epidemiology.^[31] For multivariate settings, the Dirichlet distribution extends the Beta as a conjugate prior in Dirichlet-multinomial models for categorical or compositional data, with density f(\mathbf{x} \mid \boldsymbol{\alpha}) = \frac{1}{B(\boldsymbol{\alpha})} \prod_{i=1}^K x_i^{\alpha_i - 1}, \quad x_i > 0, \sum x_i = 1, where B(\boldsymbol{\alpha}) = \prod_{i=1}^K \Gamma(\alpha_i) / \Gamma(\sum \alpha_i) is the multivariate Beta function and \boldsymbol{\alpha} = (\alpha_1, \dots, \alpha_K) with \alpha_i > 0.^[32] In Dirichlet-multinomial models, the Dirichlet prior on the multinomial probability vector leads to a posterior that is also Dirichlet, facilitating inference in applications like topic modeling and species abundance estimation.^[33] Moments of these distributions, particularly those involving logarithms, are computed using derivatives of the log-Gamma function via the digamma function \psi(z) = \frac{d}{dz} \ln \Gamma(z). For the Gamma distribution, the expected value of the logarithm is E[\ln X] = \psi(\alpha) - \ln \beta, which arises in entropy calculations and maximum likelihood estimation.^[34] This approach leverages the analytic properties of the Gamma function for efficient moment evaluation in Bayesian models.^[35]

In Physics and Engineering

In quantum field theory, the Gamma function, expressed through its Euler integral representation \Gamma(z) = \int_0^\infty t^{z-1} e^{-t} \, dt, plays a crucial role in evaluating Gaussian integrals that underpin Feynman path integrals for free fields. These integrals arise naturally in the path integral formulation, where the propagator for scalar fields involves completions to the square, yielding factors proportional to \Gamma(1/2) = \sqrt{\pi}, essential for normalizing quantum fluctuations in both non-relativistic quantum mechanics and relativistic field theories.^[36]^[37] Furthermore, in renormalization procedures using dimensional regularization, loop integrals are analytically continued to d dimensions, resulting in expressions that factorize into products of Gamma functions to handle ultraviolet divergences. For instance, the one-loop self-energy in \phi^4 theory yields terms like \Gamma(2 - d/2) \Gamma(d/2 - 1)^2 / \Gamma(d - 2), which isolate poles at d=4 for counterterm subtraction, enabling finite predictions for physical observables.^[38]^[39] The Beta function, via its Euler integral form B(m,n) = \int_0^1 t^{m-1} (1-t)^{n-1} \, dt = \Gamma(m) \Gamma(n) / \Gamma(m+n), facilitates the computation of multi-propagator loop integrals through Feynman parametrization, where denominators are combined into a single parameter integral that evaluates to a Beta function. This technique is pivotal for calculating scattering amplitudes, such as the four-point function in scalar theories, reducing complex momentum integrals to manageable forms that reveal the theory's perturbative structure.^[40]^[41] In control theory, particularly for fractional-order systems, the Gamma function appears in the definition of fractional derivatives and integrals, influencing the stability analysis of transfer functions. For a fractional integrator with transfer function G(s) = 1 / s^\alpha where $0 < \alpha < 1, the Mittag-Leffler stability criterion involves Gamma-related terms in the characteristic equation, ensuring asymptotic stability when the argument of the system's eigenvalues lies within a wedge determined by \alpha, as derived from the fractional Laplace transform properties.^[42]^[43] In signal processing applications, numerical approximations of the incomplete Gamma function \gamma(s,x) = \int_0^x t^{s-1} e^{-t} \, dt are employed to compute performance metrics in fading channels modeled by the Nakagami-m distribution, such as bit error rates in wireless communications. Efficient algorithms, like continued fraction expansions or series summations, enable real-time evaluation for outage probabilities, where the cumulative distribution function is F(r) = \gamma(m, m r^2 / \Omega) / \Gamma(m), optimizing receiver designs under multipath conditions without excessive computational overhead.^[44]^[45]

References

[1]
[PDF] Euler's integrals, PHYS 2400 - UConn Physics Department
Jan 29, 2025 · Integral Eq. (2) is known as the Euler integral of the second kind. Using the relation e−t dt = −d e−t and integrating Eq. (2) by parts ...
[2]
[PDF] THE EULERIAN FUNCTIONS - Applied Mathematics
Euler integral of the first kind, while the integral representation (G.1) for Γ(z) is referred to as the Euler integral of the second kind. The Beta ...
[3]
Gamma Function - an overview | ScienceDirect Topics
Its solution, the gamma function, is contained in Euler's letter of October 13, 1729, to Goldbach [AAR]. In the 19th century, the definition of the gamma ...
[4]
DLMF: §5.12 Beta Function ‣ Properties ‣ Chapter 5 Gamma ...
In this section all fractional powers have their principal values, except where noted otherwise. In (5.12.1)–(5.12.4) it is assumed R a > 0 and R b > 0.
[5]
Beta Function -- from Wolfram MathWorld
... integral (also called the Eulerian integral of the first kind). It is defined by B(p,q)=(Gamma(p)Gamma(q))/(Gamma(p+q))=((p-1)!(q-1)!)/((p+q-1)!). (1) The ...
[6]
DLMF: §5.2 Definitions ‣ Properties ‣ Chapter 5 Gamma Function
When R ⁡ z ≤ 0 , Γ ⁡ ( z ) is defined by analytic continuation. It is a meromorphic function with no zeros, and with simple poles of residue.<|separator|>
[7]
Leonhard Euler's Integral: A Historical Profile of the Gamma Function
Many people think that mathematical ideas are static. They think that the ideas briginated at some time in the historical past and remain unchanged for.
[8]
[PDF] Introduction to the Gamma Function
Feb 4, 2002 · From this theorem, we see that the gamma function Γ(x) (or the Eulerian integral of the second kind) is well defined and analytic for x > 0 (and ...
[9]
[PDF] Gamma the function - How Euler Did It
"Leonhard Euler's Integral: A Historical Profile of the Gamma Function", The American. Mathematical Monthly, Vol. 66, No. 10 (Dec., 1959), pp. 849-869. [E19] ...
[10]
[PDF] A Historical Profile of the Gamma Function - OSU Math
Euler's integral appears everywhere and is inextricably bound to a host of special functions. Its frequency and simplicity make it fundamental. When the ...
[11]
De progressionibus transcendentibus seu quarum termini generales ...
Petersburg Academy on November 28, 1729. Euler gave the essential content of this treatise to his friend Goldbach in a letter on January 8, 1730.
[12]
"Introductio in analysin infinitorum, volume 1" by Leonhard Euler
Sep 25, 2018 · In the Introductio in analysin infinitorum (this volume, together with E102), Euler lays the foundations of modern mathematical analysis. He ...Missing: 1738 Beta
[13]
[PDF] Euler's Introductio in analysin infinitorum and the ... - HAL-SHS
Jul 13, 2007 · [...] in the Introductio in analysin infinitorum, Euler placed the concept of function as one of the pillars of analysis and this discipline, ...
[14]
[PDF] Euler and the multiplication formula for the Gamma Function
The interpolation of the factorial by the r-function was found nearly simultaneously by Daniel Bernoulli (1700-1782) [Be29] and Euler in 1729 (see also his ...
[15]
A Course of Modern Analysis [5 ed.] 1316518930, 9781316518939
... Whittaker and Watson” belongs to this select group. In fact there were two ... Euler's integral for the Gamma-function. 13.13 The expression of ζ(s, a) ...
[16]
DLMF: §5.5 Functional Relations ‣ Properties ‣ Chapter 5 Gamma ...
§5.5(ii) Reflection ; z ≠ 0 , ± 1 , … , ; ⓘ. Symbols: Γ ⁡ ( z ) : gamma function, π : the ratio of the circumference of a circle to its diameter, sin ⁡ z : sine ...
[17]
https://dlmf.nist.gov/5.5
[18]
DLMF: §5.4 Special Values and Extrema ‣ Properties ‣ Chapter 5 Gamma Function
### Summary of Analytic Continuation, Reflection Formula, Poles, and Asymptotic Behavior of the Gamma Function
[19]
5.9 Integral Representations ‣ Properties ‣ Chapter 5 Gamma ...
To increase the region of validity of this equation, the logarithm of the gamma function that appears on its left-hand side has been changed to Ln ⁡ Γ ⁡ ( z + ...
[20]
[PDF] Euler's Gamma Function and the Field with One Element
Euler's Gamma function is the analytic continuation of the Mellin transform of the negative exponential function: The Gamma function has no zeroes, and its ...
[21]
[PDF] Schlömilch integrals and probability distributions on the simplex - arXiv
Nov 2, 2022 · The Schlömilch integral, a generalization of the Dirichlet integral on the simplex, and related probability distributions are reviewed.
[22]
[PDF] arXiv:0710.3981v1 [math.CA] 22 Oct 2007
Oct 22, 2007 · One of these is the Selberg integral, an n-dimensional generalization of the Euler beta integral. We trace its sudden rise to prominence, ...
[23]
None
### Summary of Integral Representation, Euler Integrals, and Convergence of Lauricella’s F_C
[24]
[PDF] The Beta and Dirichlet Distributions 7.4.1 The Beta Random Variable
α − 1. (α − 1) + (β − 1). Also note the following: • The first term in the pdf,. 1. B(α,β) is just a normalizing constant (ensures the pdf to integrates to. 1) ...
[25]
[PDF] Inference Probability - CMU School of Computer Science
Here the normalizing constant B(α, β) is the beta function: B(α, β) = Z 1. 0 ... The posterior is therefore another beta distribution with parameters (α+x, β +n−x)! ...
[26]
[PDF] Unit 3: Bayesian Inference with Conjugate Pairs
• Gamma family of prior distributions is closed under sampling from the Poisson likelihood ... • A gamma prior distribution is conjugate to the Poisson likelihood.
[27]
[PDF] The Gamma/Poisson Bayesian Model
... Poisson(λ), then a gamma(α, β) prior on λ is a conjugate prior. Likelihood: L(λ|x) = n. Y i=1 e−λλxi xi ! = e−nλλPxi. Qn i=1(xi !) Prior: p(λ) =.
[28]
[PDF] Chapter 9 The exponential family: Conjugate priors - People @EECS
The corresponding conjugate prior retains the shape of the Poisson likelihood: p(θ |α) ∝ θα1−1e−α2θ,. (9.32) which we recognize as the gamma distribution:.
[29]
[PDF] Unit 9: Multinomial Distribution and Latent Groups
• The multinomial/Dirichlet conjugate pair generalizes the binomial / beta ... • Dirichlet distribution is a multivariate generalization of the Beta.
[30]
[PDF] Mixed-membership Models (and an Introduction to Variational ...
Nov 20, 2016 · The Dirichlet is the multivariate extension of the beta distribution, ... The Dirichlet is conjugate to the multinomial. Let z be an ...<|separator|>
[31]
[PDF] Latent Confusion Analysis by Normalized Gamma Construction
... E[log x] = Ψ(a) − log b. x ∼ P expresses that a random variable x is ... gℓ is distributed by the gamma distribution. We typically need the ...
[32]
[PDF] Topic 15 Maximum Likelihood Estimation - Arizona Math
We return to the model of the gamma distribution for the distribution of fitness effects ... tive of the logarithm of the gamma function ψ(α) = d dα ln Γ(α).<|control11|><|separator|>
[33]
[PDF] Path Integrals in Quantum Mechanics - MIT
Following this, we will introduce the concept of Euclidean path integrals and discuss further uses of the path integral formulation in the field of statistical ...<|control11|><|separator|>
[34]
[PDF] 3 The Feynman Path Integral in Field Theory - UF Physics
We now want to generalize the path integral to Field Theory. Reasoning by analogy with Quantum Mechanics, and taking for convenience a real scalar.
[35]
[PDF] Lecture 23 Regularization II: Dimensional Regularization
In the last equality we used the integral definition of the Gamma function. From ... Quantum Field Theory, by C. Itzykson and J. Zuber, Section 8.1.2.
[36]
Regularization, renormalization, and dimensional analysis
Mar 1, 2011 · ... Γ is the Gamma function ... dimensional regularization and renormalization that we encounter in quantum field theory radiative calculations.<|control11|><|separator|>
[37]
[PDF] Quantum Field Theory - Physics - Boston University
May 16, 2021 · so the integral is in the standard form of Euler's Beta function B(a, b) = Γ(a)Γ(b). Γ(a+b) . Putting it all together, we have. G4. = − λ2. 2.
[38]
[PDF] Introduction to Loop Calculations
The integration parameters zi are called Feynman parameters. For a generic one-loop diagram as shown above we have νi = 1 ∀i. Simple example: one-loop two- ...
[39]
[PDF] The Controllability, Observability, and Stability Analysis of a Class of ...
where m is an integer satisfying m − 1 < α ≤ m, m ∈ N, and Γ(·) is the Gamma function. ... Then we design a supervisory controller with the transfer function G0(s) ...
[40]
Stability properties for generalized fractional differential systems
where ; is the well-known Euler Gamma function. With this notation, the fractional integral of order of a causal function or causal distribution f is: I f ...
[41]
Distortion outage minimization in Nakagami fading using limited ...
Oct 25, 2011 · For Nakagami-m fading, the c.d.f is given by the regularized lower incomplete Gamma function defined as F1(s1,j) = γ(mλs1,j, m)/Γ(m) where γ ( ...
[42]
https://mechatronics.ucmerced.edu/sites/g/files/ufvvjh1226/f/page/documents/08707135.pdf