GammaRegularized[a,z]
is the regularized incomplete gamma function
.
GammaRegularized
GammaRegularized[a,z]
is the regularized incomplete gamma function
.
Details
- Mathematical function, suitable for both symbolic and numerical manipulation.
- In non‐singular cases,
. - GammaRegularized[a,z0,z1] is the generalized regularized incomplete gamma function, defined in non‐singular cases as Gamma[a,z0,z1]/Gamma[a].
- Note that the arguments in GammaRegularized are arranged differently from those in BetaRegularized.
- For certain special arguments, GammaRegularized automatically evaluates to exact values.
- GammaRegularized can be evaluated to arbitrary numerical precision.
- GammaRegularized automatically threads over lists.
- GammaRegularized can be used with Interval and CenteredInterval objects. »
Examples
open all close allBasic Examples (5)
GammaRegularized[1, 1.5]Plot over a subset of the reals:
Plot[{GammaRegularized[1, z], GammaRegularized[2, z], GammaRegularized[3, z]}, {z, -3, 3}]Plot over a subset of the complexes:
ComplexPlot3D[GammaRegularized[2, z], {z, -2 - 2I, 2 + 2I}, PlotLegends -> Automatic]Series expansion at the origin:
Series[GammaRegularized[n, z], {z, 0, 2}]Series expansion at Infinity:
Series[GammaRegularized[ a, x], {x, ∞, 3}]//NormalScope (41)
Numerical Evaluation (6)
{GammaRegularized[2, 3.3], GammaRegularized[2, 3.3, 3.4]}Evaluate numerically to high precision:
N[GammaRegularized[2, 33 / 10], 50]The precision of the output tracks the precision of the input:
GammaRegularized[2, 3.30000000000000000000000000000]Evaluate numerically for complex arguments:
N[GammaRegularized[2 + 3I, 4 - I]]Evaluate GammaRegularized efficiently at high precision:
GammaRegularized[2, 2.2`500]//TimingGammaRegularized[2, 2.2`10000];//TimingCompute worst-case guaranteed intervals using Interval and CenteredInterval objects:
GammaRegularized[2 / 5, Interval[{0.21, 0.22}]]GammaRegularized[2 / 5, CenteredInterval[1 / 4, 1 / 100]]GammaRegularized[2 / 5, CenteredInterval[-3 - I, (1 + I) / 100]]Compute average-case statistical intervals using Around:
GammaRegularized[1, 1, Around[1 / 2, 0.01]]Compute the elementwise values of an array:
GammaRegularized[2, {{7 / 2, 0}, {0, 13 / 2}}]Or compute the matrix GammaRegularized function using MatrixFunction:
MatrixFunction[GammaRegularized[2, #]&, {{7 / 2, 0}, {0, 13 / 2}}]Specific Values (5)
Table[GammaRegularized[1, x], {x, 0, 3}]GammaRegularized[1, {-Infinity, Infinity}]Evaluate at integer and half‐integer arguments:
GammaRegularized[3, z]//FunctionExpand //SimplifyGammaRegularized[13 / 2, z]//FunctionExpand//SimplifyThe generalized regularized incomplete gamma function at integer and half‐integer arguments:
GammaRegularized[5, 3, z2]//FunctionExpand//SimplifyGammaRegularized[13 / 2, 7 / 2, z2]//FunctionExpand//Simplifyxzero = Solve[GammaRegularized[2, x] == 0 && -2 < x < 0, x][[1, 1, 2]]//QuietPlot[GammaRegularized[2, x], {x, -2, 4}, Epilog -> Style[Point[{xzero, GammaRegularized[2, xzero]}], PointSize[Large], Red]]Visualization (3)
Plot the regularized gamma function for integer arguments:
Plot[{GammaRegularized[1, x], GammaRegularized[2, x], GammaRegularized[3, x], GammaRegularized[4, x]}, {x, -3, 3}]Plot the regularized gamma function for half-integer arguments:
Plot[{GammaRegularized[1 / 2, x], GammaRegularized[3 / 2, x], GammaRegularized[-1 / 2, x], GammaRegularized[-3 / 2, x]}, {x, 0, 4}, IconizedObject[«PlotOptions»]]ComplexContourPlot[Re[GammaRegularized[3, z]], {z, -3 - 3I, 3 + 3I}, Contours -> 24]ComplexContourPlot[Im[GammaRegularized[3, z]], {z, -3 - 3I, 3 + 3I}, Contours -> 24]Function Properties (9)
FunctionDomain[GammaRegularized[a, x], x]FunctionDomain[GammaRegularized[a, z], z, Complexes]The regularized incomplete gamma function
achieves all positive real values for real inputs:
FunctionRange[GammaRegularized[1, x], x, y]FunctionRange[GammaRegularized[1, z], z, y, Complexes]FunctionRange[GammaRegularized[1 / 2, x], x, y]
is an analytic function of
for positive integer
:
FunctionAnalytic[GammaRegularized[a, x], x, Assumptions -> a∈ℤ && a > 0]For other values of
, it may or may not be analytic:
FunctionAnalytic[GammaRegularized[-2, x], x]FunctionAnalytic[GammaRegularized[1 / 2, x], x]When it is not analytic, it is also not meromorphic:
FunctionMeromorphic[GammaRegularized[1 / 2, x], x]
has no singularities or discontinuities:
FunctionSingularities[GammaRegularized[1, x], x]FunctionDiscontinuities[GammaRegularized[1, x], x]
has singularities and discontinuities for
:
FunctionSingularities[GammaRegularized[1 / 2, x], x]FunctionDiscontinuities[GammaRegularized[1 / 2, x], x]
is a non-increasing function of
when
is a positive, odd integer:
FunctionMonotonicity[GammaRegularized[2k + 1, x], x, Assumptions -> k∈ℤ && k > 0]But in general, it is neither non-increasing nor non-decreasing:
FunctionMonotonicity[GammaRegularized[2, x], x]
is an injective function of
for noninteger
:
FunctionInjective[GammaRegularized[a, x], x, Assumptions -> a > 0 && a∉ℤ]For other values of
, it may or may not be injective in
:
FunctionInjective[GammaRegularized[1, x], x]FunctionInjective[GammaRegularized[2, x], x]Plot[{GammaRegularized[1 / 2, x], GammaRegularized[1, x], GammaRegularized[2, x], .5}, {x, -1, 3}, PlotRange -> {0, 2}]
is not a surjective function of
for most values of
:
FunctionSurjective[GammaRegularized[a, x], x, Assumptions -> a > 0 && a∉ℤ]Table[FunctionSurjective[GammaRegularized[a, x], x] , {a, 0, 4}]Plot[{GammaRegularized[1, x], -1}, {x, 0, 10}]
is non-negative for positive odd
:
FunctionSign[GammaRegularized[2k + 1, x], x, Assumptions -> k∈ℤ && k > 0]In general, it is neither non-negative nor non-positive:
FunctionSign[GammaRegularized[2, x], x]FunctionConvexity[GammaRegularized[1, x], x]
is concave on its real domain:
FunctionConvexity[{GammaRegularized[-1 / 2, x], x > 0}, x]
is neither convex nor concave:
FunctionConvexity[{GammaRegularized[2, x], x > 0}, x]Differentiation (2)
First derivative of the regularized incomplete gamma function:
D[GammaRegularized[a, x], x]derivs = Table[D[GammaRegularized[a, x], {x, n}], {n, 1, 3}]Plot higher derivatives for integer and half-integer
:
Plot[Evaluate[derivs /. a -> 2], {x, -4, 4}, PlotLegends -> {"First Derivative", "Second Derivative", "Third Derivative"}]
Plot[Evaluate[derivs /. a -> 3 / 2], {x, 0, 4}, PlotLegends -> {"First Derivative", "Second Derivative", "Third Derivative"}]Integration (3)
Indefinite integral of the regularized incomplete gamma function:
Integrate[GammaRegularized[a, x], x]Integrate[GammaRegularized[a, x], {x, 0, Infinity}]Integrate[Exp[x]GammaRegularized[a, x], x]Integrate[x ^ b GammaRegularized[a, x], x]Series Expansions (4)
Series expansion for the regularized incomplete gamma function:
Series[GammaRegularized[a, x], {x, 0, 3}]Plot the first three approximations for
around
:
terms = Normal@Table[Series[GammaRegularized[1, x], {x, 0, m}], {m, 1, 3}];
Plot[{GammaRegularized[1, x], terms}, {x, -2, 2}]Series[GammaRegularized[a, z], {z, Infinity, 2}]//NormalGive the result for an arbitrary symbolic direction:
Series[GammaRegularized[a, x], {x, DirectedInfinity[z], 2}]//NormalExpansions of the generalized regularized incomplete gamma function at a generic point:
Series[GammaRegularized[a, z1, z2], {z1, z10, 2}]Series[GammaRegularized[a, z1, z2], {a, a0, 1}]GammaRegularized can be applied to a power series:
GammaRegularized[a, 1 + x + (x^2/2) + O[x]^4]//SimplifyIntegral Transforms (2)
Compute the Laplace transform using LaplaceTransform:
LaplaceTransform[GammaRegularized[a, t], t, s ]MellinTransform[GammaRegularized[a, x], x, s ]Function Identities and Simplifications (3)
FunctionExpand regularized gamma functions through ordinary gamma functions:
FunctionExpand[GammaRegularized[a, z]]Use FullSimplify to simplify regularized gamma functions:
FullSimplify[a Gamma[a - 1] GammaRegularized[a, z] - Gamma[a, z]]GammaRegularized[a, z] == GammaRegularized[a + 1, z] - (1/Gamma[a + 1])Exp[-z] z^a//FullSimplifyFunction Representations (4)
Integral representation of the regularized incomplete gamma:
GammaRegularized[a, x] == (1/Gamma[a])Integrate[t ^ (a - 1) Exp[-t], {t, x, Infinity}, Assumptions -> x ≥ 0]//FullSimplifyRepresentation in terms of MeijerG:
MeijerGReduce[GammaRegularized[a, x], x]GammaRegularized can be represented as a DifferentialRoot:
DifferentialRootReduce[GammaRegularized[a, x], x]TraditionalForm formatting:
GammaRegularized[n, z]//TraditionalFormGeneralizations & Extensions (4)
Regularized Incomplete Gamma Function (3)
Evaluate at integer and half‐integer arguments:
GammaRegularized[3, z]//FunctionExpand//SimplifyGammaRegularized[13 / 2, z]//FunctionExpand//SimplifyInfinite arguments give symbolic results:
GammaRegularized[0, Infinity]GammaRegularized threads element‐wise over lists:
GammaRegularized[{2, 3, 5, 7, 11}, 2.5]Applications (5)
Plot of the real part of GammaRegularized over the complex plane:
Plot3D[Re[GammaRegularized[-7 / 2, x + I y]], {x, -3, 2}, {y, -1, 1}]Plot3D[Re[GammaRegularized[ax + I ay, -2 - I]], {ax, -5, 2}, {ay, -1, 1}]CDF[ChiSquareDistribution[r]][x]{D[%, x], PDF[ChiSquareDistribution[r]][x]}Plot the CDFs for various degrees of freedom:
Plot[Evaluate[Table[%%, {r, 5}]], {x, 0, 10}]CDF of the gamma distribution:
CDF[GammaDistribution[α, θ]][x]{D[% , x], PDF[GammaDistribution[α, θ]][x]}Plot the CDFs for various parameters:
Plot[Evaluate[Table[%%, {α, 3}, {θ, 4}]], {x, 0, 12}]Fractional derivatives/integrals of the exponential function:
fd[α_, x_] := Exp[x](1 - GammaRegularized[-α, x])Check that this is the defining Riemann–Liouville integral:
(Integrate[Exp[x](y - x) ^ (-α - 1) / Gamma[-α], {x, 0, y}, GenerateConditions -> False] /. y -> x) - fd[α, x]//FullSimplifyFractional derivative/integral of integer orders:
Table[fd[k, x], {k, -2, 2}]//FullSimplifyPlot fractional derivative/integral:
Plot3D[Re[fd[α, x]], {x, -1, 1}, {α, -1, 1}]A liquid crystal display (LCD) has 1920×1080 pixels. A display is accepted if it has 15 or fewer faulty pixels. The probability that a pixel is faulty from production is
. Find the proportion of displays that are accepted:
NProbability[n ≤ 15, nPoissonDistribution[1920 * 1080 * 5 * 10^-6]]Find the pixel failure rate required to produce 4000×2000 pixel displays and still have an acceptance rate of at least 90%:
prob = Probability[n ≤ 15, nPoissonDistribution[4000 * 2000 λ]]Plot the acceptance rate as a function of the pixel failure rate:
LogLogPlot[prob, {λ, 10^-7, 10^-5}, GridLines -> All, Frame -> True, FrameLabel -> {"Pixel Failure Rate", None}, PlotLabel -> "LCD Acceptance Rate"]Find the maximal acceptable pixel failure rate:
Maximize[{λ, prob ≥ 9 / 10 && 10^-6 < λ < 2 * 10^-6}, λ, Reals]//NCDF[PoissonDistribution[4000 * 2000 λ], 15] /. λ -> First[%]Properties & Relations (4)
Use FullSimplify to simplify regularized gamma functions:
FullSimplify[a Gamma[a - 1] GammaRegularized[a, z] - Gamma[a, z]]Use FunctionExpand to express regularized gamma functions through ordinary gamma functions:
FunctionExpand[GammaRegularized[a, z]]Solve a transcendental equation:
Solve[GammaRegularized[α, x] ^ 2 - GammaRegularized[α, x] == r, x]Numerically find a root of a transcendental equation:
FindRoot[GammaRegularized[3, z] - 4 GammaRegularized[2, z + 2] + z == 6, {z, 1}]Possible Issues (3)
Large arguments can underflow and produce a machine zero:
GammaRegularized[10, 10. ^ 10]Machine‐number inputs can give high‐precision results:
GammaRegularized[800., 800. I]MachineNumberQ[%]Gamma rather than GammaRegularized is usually generated in computations:
HypergeometricPFQRegularized[{a}, {a + 1}, -z]Regularized gamma functions are typically not generated by FullSimplify:
FunctionExpand[GammaRegularized[a, z]]FullSimplify[%]Neat Examples (3)
Nest GammaRegularized over the complex plane:
DensityPlot[Arg[Nest[GammaRegularized[#, 1 / #]&, x + I y, 2]], {x, -1, 1}, {y, -1, 1}, Exclusions -> {}]//QuietPlot GammaRegularized at infinity:
DensityPlot[Arg[GammaRegularized[4 / (x + I y), 1.3]], {x, -1, 1}, {y, -1, 1}, Exclusions -> {}]Riemann surface of the incomplete regularized gamma function:
ParametricPlot3D[Evaluate[Table[{r Cos[φ], r Sin[φ], Re[Exp[2 k Pi I / 3] GammaRegularized[1 / 3, r Exp[I φ]] + (1 - Exp[2 k Pi I / 3])]}, {k, -2, 2}]], {r, 0, 2}, {φ, -Pi, Pi}, Exclusions -> {}]Tech Notes
Related Guides
Related Links
History
Introduced in 1991 (2.0) | Updated in 2021 (13.0) ▪ 2022 (13.1)
Text
Wolfram Research (1991), GammaRegularized, Wolfram Language function, https://reference.wolfram.com/language/ref/GammaRegularized.html (updated 2022).
CMS
Wolfram Language. 1991. "GammaRegularized." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2022. https://reference.wolfram.com/language/ref/GammaRegularized.html.
APA
Wolfram Language. (1991). GammaRegularized. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/GammaRegularized.html
BibTeX
@misc{reference.wolfram_2026_gammaregularized, author="Wolfram Research", title="{GammaRegularized}", year="2022", howpublished="\url{https://reference.wolfram.com/language/ref/GammaRegularized.html}", note=[Accessed: 13-June-2026]}
BibLaTeX
@online{reference.wolfram_2026_gammaregularized, organization={Wolfram Research}, title={GammaRegularized}, year={2022}, url={https://reference.wolfram.com/language/ref/GammaRegularized.html}, note=[Accessed: 13-June-2026]}