In statistics , the generalized Marcum Q-function of order ν {\displaystyle \nu } is defined as
Q ν ( a , b ) = 1 a ν − 1 ∫ b ∞ x ν exp ( − x 2 + a 2 2 ) I ν − 1 ( a x ) d x {\displaystyle Q_{\nu }(a,b)={\frac {1}{a^{\nu -1}}}\int _{b}^{\infty }x^{\nu }\exp \left(-{\frac {x^{2}+a^{2}}{2}}\right)I_{\nu -1}(ax)\,dx} where b ≥ 0 {\displaystyle b\geq 0} and a , ν > 0 {\displaystyle a,\nu >0} and I ν − 1 {\displaystyle I_{\nu -1}} is the modified Bessel function of first kind of order ν − 1 {\displaystyle \nu -1} . If b > 0 {\displaystyle b>0} , the integral converges for any ν {\displaystyle \nu } . The Marcum Q-function occurs as a complementary cumulative distribution function for noncentral chi , noncentral chi-squared , and Rice distributions . In engineering, this function appears in the study of radar systems, communication systems, queueing system, and signal processing. This function was first studied for ν = 1 {\displaystyle \nu =1} , and hence named after, by Jess Marcum for pulsed radars.[ 1]
Finite integral representation [ edit ] Using the fact that Q ν ( a , 0 ) = 1 {\displaystyle Q_{\nu }(a,0)=1} , the generalized Marcum Q-function can alternatively be defined as a finite integral as
Q ν ( a , b ) = 1 − 1 a ν − 1 ∫ 0 b x ν exp ( − x 2 + a 2 2 ) I ν − 1 ( a x ) d x . {\displaystyle Q_{\nu }(a,b)=1-{\frac {1}{a^{\nu -1}}}\int _{0}^{b}x^{\nu }\exp \left(-{\frac {x^{2}+a^{2}}{2}}\right)I_{\nu -1}(ax)\,dx.} However, it is preferable to have an integral representation of the Marcum Q-function such that (i) the limits of the integral are independent of the arguments of the function, (ii) and that the limits are finite, (iii) and that the integrand is a Gaussian function of these arguments. For positive integer values of ν = n {\displaystyle \nu =n} , such a representation is given by the trigonometric integral[ 2] [ 3]
Q n ( a , b ) = { H n ( a , b ) a < b , 1 2 + H n ( a , a ) a = b , 1 + H n ( a , b ) a > b , {\displaystyle Q_{n}(a,b)=\left\{{\begin{array}{lr}H_{n}(a,b)&a<b,\\{\frac {1}{2}}+H_{n}(a,a)&a=b,\\1+H_{n}(a,b)&a>b,\end{array}}\right.} where
H n ( a , b ) = ζ 1 − n 2 π exp ( − a 2 + b 2 2 ) ∫ 0 2 π cos ( n − 1 ) θ − ζ cos n θ 1 − 2 ζ cos θ + ζ 2 exp ( a b cos θ ) d θ , {\displaystyle H_{n}(a,b)={\frac {\zeta ^{1-n}}{2\pi }}\exp \left(-{\frac {a^{2}+b^{2}}{2}}\right)\int _{0}^{2\pi }{\frac {\cos(n-1)\theta -\zeta \cos n\theta }{1-2\zeta \cos \theta +\zeta ^{2}}}\exp(ab\cos \theta )\mathrm {d} \theta ,} and the ratio ζ = a / b {\displaystyle \zeta =a/b} is a constant.
For any real ν > 0 {\displaystyle \nu >0} , such finite trigonometric integral is given by[ 4]
Q ν ( a , b ) = { H ν ( a , b ) + C ν ( a , b ) a < b , 1 2 + H ν ( a , a ) + C ν ( a , b ) a = b , 1 + H ν ( a , b ) + C ν ( a , b ) a > b , {\displaystyle Q_{\nu }(a,b)=\left\{{\begin{array}{lr}H_{\nu }(a,b)+C_{\nu }(a,b)&a<b,\\{\frac {1}{2}}+H_{\nu }(a,a)+C_{\nu }(a,b)&a=b,\\1+H_{\nu }(a,b)+C_{\nu }(a,b)&a>b,\end{array}}\right.} where H n ( a , b ) {\displaystyle H_{n}(a,b)} is as defined before, ζ = a / b {\displaystyle \zeta =a/b} , and the additional correction term is given by
C ν ( a , b ) = sin ( ν π ) π exp ( − a 2 + b 2 2 ) ∫ 0 1 ( x / ζ ) ν − 1 ζ + x exp [ − a b 2 ( x + 1 x ) ] d x . {\displaystyle C_{\nu }(a,b)={\frac {\sin(\nu \pi )}{\pi }}\exp \left(-{\frac {a^{2}+b^{2}}{2}}\right)\int _{0}^{1}{\frac {(x/\zeta )^{\nu -1}}{\zeta +x}}\exp \left[-{\frac {ab}{2}}\left(x+{\frac {1}{x}}\right)\right]\mathrm {d} x.} For integer values of ν {\displaystyle \nu } , the correction term C ν ( a , b ) {\displaystyle C_{\nu }(a,b)} tend to vanish.
Monotonicity and log-concavity [ edit ] The generalized Marcum Q-function Q ν ( a , b ) {\displaystyle Q_{\nu }(a,b)} is strictly increasing in ν {\displaystyle \nu } and a {\displaystyle a} for all a ≥ 0 {\displaystyle a\geq 0} and b , ν > 0 {\displaystyle b,\nu >0} , and is strictly decreasing in b {\displaystyle b} for all a , b ≥ 0 {\displaystyle a,b\geq 0} and ν > 0. {\displaystyle \nu >0.} [ 5] The function ν ↦ Q ν ( a , b ) {\displaystyle \nu \mapsto Q_{\nu }(a,b)} is log-concave on [ 1 , ∞ ) {\displaystyle [1,\infty )} for all a , b ≥ 0. {\displaystyle a,b\geq 0.} [ 5] The function b ↦ Q ν ( a , b ) {\displaystyle b\mapsto Q_{\nu }(a,b)} is strictly log-concave on ( 0 , ∞ ) {\displaystyle (0,\infty )} for all a ≥ 0 {\displaystyle a\geq 0} and ν > 1 {\displaystyle \nu >1} , which implies that the generalized Marcum Q-function satisfies the new-is-better-than-used property.[ 6] The function a ↦ 1 − Q ν ( a , b ) {\displaystyle a\mapsto 1-Q_{\nu }(a,b)} is log-concave on [ 0 , ∞ ) {\displaystyle [0,\infty )} for all b , ν > 0. {\displaystyle b,\nu >0.} [ 5] Series representation [ edit ] The generalized Marcum Q function of order ν > 0 {\displaystyle \nu >0} can be represented using incomplete Gamma function as[ 7] [ 8] [ 9] Q ν ( a , b ) = 1 − e − a 2 / 2 ∑ k = 0 ∞ 1 k ! γ ( ν + k , b 2 2 ) Γ ( ν + k ) ( a 2 2 ) k , {\displaystyle Q_{\nu }(a,b)=1-e^{-a^{2}/2}\sum _{k=0}^{\infty }{\frac {1}{k!}}{\frac {\gamma (\nu +k,{\frac {b^{2}}{2}})}{\Gamma (\nu +k)}}\left({\frac {a^{2}}{2}}\right)^{k},} where γ ( s , x ) {\displaystyle \gamma (s,x)} is the lower incomplete Gamma function . This is usually called the canonical representation of the ν {\displaystyle \nu } -th order generalized Marcum Q-function. Q ν ( a , b ) = 1 − e − a 2 / 2 ∑ k = 0 ∞ ( − 1 ) k L k ( ν − 1 ) ( a 2 2 ) Γ ( ν + k + 1 ) ( b 2 2 ) k + ν , {\displaystyle Q_{\nu }(a,b)=1-e^{-a^{2}/2}\sum _{k=0}^{\infty }(-1)^{k}{\frac {L_{k}^{(\nu -1)}({\frac {a^{2}}{2}})}{\Gamma (\nu +k+1)}}\left({\frac {b^{2}}{2}}\right)^{k+\nu },} where L k ( α ) ( ⋅ ) {\displaystyle L_{k}^{(\alpha )}(\cdot )} is the generalized Laguerre polynomial of degree k {\displaystyle k} and of order α {\displaystyle \alpha } . The generalized Marcum Q-function of order ν > 0 {\displaystyle \nu >0} can also be represented as Neumann series expansions[ 4] [ 8] Q ν ( a , b ) = e − ( a 2 + b 2 ) / 2 ∑ α = 1 − ν ∞ ( a b ) α I − α ( a b ) , {\displaystyle Q_{\nu }(a,b)=e^{-(a^{2}+b^{2})/2}\sum _{\alpha =1-\nu }^{\infty }\left({\frac {a}{b}}\right)^{\alpha }I_{-\alpha }(ab),} 1 − Q ν ( a , b ) = e − ( a 2 + b 2 ) / 2 ∑ α = ν ∞ ( b a ) α I α ( a b ) , {\displaystyle 1-Q_{\nu }(a,b)=e^{-(a^{2}+b^{2})/2}\sum _{\alpha =\nu }^{\infty }\left({\frac {b}{a}}\right)^{\alpha }I_{\alpha }(ab),} where the summations are in increments of one. Note that when α {\displaystyle \alpha } assumes an integer value, we have I α ( a b ) = I − α ( a b ) {\displaystyle I_{\alpha }(ab)=I_{-\alpha }(ab)} . For non-negative half-integer values ν = n + 1 / 2 {\displaystyle \nu =n+1/2} , we have a closed form expression for the generalized Marcum Q-function as[ 8] [ 10] Q n + 1 / 2 ( a , b ) = 1 2 [ e r f c ( b − a 2 ) + e r f c ( b + a 2 ) ] + e − ( a 2 + b 2 ) / 2 ∑ k = 1 n ( b a ) k − 1 / 2 I k − 1 / 2 ( a b ) , {\displaystyle Q_{n+1/2}(a,b)={\frac {1}{2}}\left[\mathrm {erfc} \left({\frac {b-a}{\sqrt {2}}}\right)+\mathrm {erfc} \left({\frac {b+a}{\sqrt {2}}}\right)\right]+e^{-(a^{2}+b^{2})/2}\sum _{k=1}^{n}\left({\frac {b}{a}}\right)^{k-1/2}I_{k-1/2}(ab),} where e r f c ( ⋅ ) {\displaystyle \mathrm {erfc} (\cdot )} is the complementary error function . Since Bessel functions with half-integer parameter have finite sum expansions as[ 4] I ± ( n + 0.5 ) ( z ) = 1 π ∑ k = 0 n ( n + k ) ! k ! ( n − k ) ! [ ( − 1 ) k e z ∓ ( − 1 ) n e − z ( 2 z ) k + 0.5 ] , {\displaystyle I_{\pm (n+0.5)}(z)={\frac {1}{\sqrt {\pi }}}\sum _{k=0}^{n}{\frac {(n+k)!}{k!(n-k)!}}\left[{\frac {(-1)^{k}e^{z}\mp (-1)^{n}e^{-z}}{(2z)^{k+0.5}}}\right],} where n {\displaystyle n} is non-negative integer, we can exactly represent the generalized Marcum Q-function with half-integer parameter. More precisely, we have[ 4] Q n + 1 / 2 ( a , b ) = Q ( b − a ) + Q ( b + a ) + 1 b 2 π ∑ i = 1 n ( b a ) i ∑ k = 0 i − 1 ( i + k − 1 ) ! k ! ( i − k − 1 ) ! [ ( − 1 ) k e − ( a − b ) 2 / 2 + ( − 1 ) i e − ( a + b ) 2 / 2 ( 2 a b ) k ] , {\displaystyle Q_{n+1/2}(a,b)=Q(b-a)+Q(b+a)+{\frac {1}{b{\sqrt {2\pi }}}}\sum _{i=1}^{n}\left({\frac {b}{a}}\right)^{i}\sum _{k=0}^{i-1}{\frac {(i+k-1)!}{k!(i-k-1)!}}\left[{\frac {(-1)^{k}e^{-(a-b)^{2}/2}+(-1)^{i}e^{-(a+b)^{2}/2}}{(2ab)^{k}}}\right],} for non-negative integers n {\displaystyle n} , where Q ( ⋅ ) {\displaystyle Q(\cdot )} is the Gaussian Q-function . Alternatively, we can also more compactly express the Bessel functions with half-integer as sum of hyperbolic sine and cosine functions:[ 11] I n + 1 2 ( z ) = 2 z π [ g n ( z ) sinh ( z ) + g − n − 1 ( z ) cosh ( z ) ] , {\displaystyle I_{n+{\frac {1}{2}}}(z)={\sqrt {\frac {2z}{\pi }}}\left[g_{n}(z)\sinh(z)+g_{-n-1}(z)\cosh(z)\right],} where g 0 ( z ) = z − 1 {\displaystyle g_{0}(z)=z^{-1}} , g 1 ( z ) = − z − 2 {\displaystyle g_{1}(z)=-z^{-2}} , and g n − 1 ( z ) − g n + 1 ( z ) = ( 2 n + 1 ) z − 1 g n ( z ) {\displaystyle g_{n-1}(z)-g_{n+1}(z)=(2n+1)z^{-1}g_{n}(z)} for any integer value of n {\displaystyle n} . Recurrence relation and generating function [ edit ] Integrating by parts, we can show that generalized Marcum Q-function satisfies the following recurrence relation[ 8] [ 10] Q ν + 1 ( a , b ) − Q ν ( a , b ) = ( b a ) ν e − ( a 2 + b 2 ) / 2 I ν ( a b ) . {\displaystyle Q_{\nu +1}(a,b)-Q_{\nu }(a,b)=\left({\frac {b}{a}}\right)^{\nu }e^{-(a^{2}+b^{2})/2}I_{\nu }(ab).} The above formula is easily generalized as[ 10] Q ν − n ( a , b ) = Q ν ( a , b ) − ( b a ) ν e − ( a 2 + b 2 ) / 2 ∑ k = 1 n ( a b ) k I ν − k ( a b ) , {\displaystyle Q_{\nu -n}(a,b)=Q_{\nu }(a,b)-\left({\frac {b}{a}}\right)^{\nu }e^{-(a^{2}+b^{2})/2}\sum _{k=1}^{n}\left({\frac {a}{b}}\right)^{k}I_{\nu -k}(ab),} Q ν + n ( a , b ) = Q ν ( a , b ) + ( b a ) ν e − ( a 2 + b 2 ) / 2 ∑ k = 0 n − 1 ( b a ) k I ν + k ( a b ) , {\displaystyle Q_{\nu +n}(a,b)=Q_{\nu }(a,b)+\left({\frac {b}{a}}\right)^{\nu }e^{-(a^{2}+b^{2})/2}\sum _{k=0}^{n-1}\left({\frac {b}{a}}\right)^{k}I_{\nu +k}(ab),} for positive integer n {\displaystyle n} . The former recurrence can be used to formally define the generalized Marcum Q-function for negative ν {\displaystyle \nu } . Taking Q ∞ ( a , b ) = 1 {\displaystyle Q_{\infty }(a,b)=1} and Q − ∞ ( a , b ) = 0 {\displaystyle Q_{-\infty }(a,b)=0} for n = ∞ {\displaystyle n=\infty } , we obtain the Neumann series representation of the generalized Marcum Q-function. The related three-term recurrence relation is given by[ 7] Q ν + 1 ( a , b ) − ( 1 + c ν ( a , b ) ) Q ν ( a , b ) + c ν ( a , b ) Q ν − 1 ( a , b ) = 0 , {\displaystyle Q_{\nu +1}(a,b)-(1+c_{\nu }(a,b))Q_{\nu }(a,b)+c_{\nu }(a,b)Q_{\nu -1}(a,b)=0,} where c ν ( a , b ) = ( b a ) I ν ( a b ) I ν + 1 ( a b ) . {\displaystyle c_{\nu }(a,b)=\left({\frac {b}{a}}\right){\frac {I_{\nu }(ab)}{I_{\nu +1}(ab)}}.} We can eliminate the occurrence of the Bessel function to give the third order recurrence relation[ 7] a 2 2 Q ν + 2 ( a , b ) = ( a 2 2 − ν ) Q ν + 1 ( a , b ) + ( b 2 2 + ν ) Q ν ( a , b ) − b 2 2 Q ν − 1 ( a , b ) . {\displaystyle {\frac {a^{2}}{2}}Q_{\nu +2}(a,b)=\left({\frac {a^{2}}{2}}-\nu \right)Q_{\nu +1}(a,b)+\left({\frac {b^{2}}{2}}+\nu \right)Q_{\nu }(a,b)-{\frac {b^{2}}{2}}Q_{\nu -1}(a,b).} Another recurrence relationship, relating it with its derivatives, is given by Q ν + 1 ( a , b ) = Q ν ( a , b ) + 1 a ∂ ∂ a Q ν ( a , b ) , {\displaystyle Q_{\nu +1}(a,b)=Q_{\nu }(a,b)+{\frac {1}{a}}{\frac {\partial }{\partial a}}Q_{\nu }(a,b),} Q ν − 1 ( a , b ) = Q ν ( a , b ) + 1 b ∂ ∂ b Q ν ( a , b ) . {\displaystyle Q_{\nu -1}(a,b)=Q_{\nu }(a,b)+{\frac {1}{b}}{\frac {\partial }{\partial b}}Q_{\nu }(a,b).} The ordinary generating function of Q ν ( a , b ) {\displaystyle Q_{\nu }(a,b)} for integral ν {\displaystyle \nu } is[ 10] ∑ n = − ∞ ∞ t n Q n ( a , b ) = e − ( a 2 + b 2 ) / 2 t 1 − t e ( b 2 t + a 2 / t ) / 2 , {\displaystyle \sum _{n=-\infty }^{\infty }t^{n}Q_{n}(a,b)=e^{-(a^{2}+b^{2})/2}{\frac {t}{1-t}}e^{(b^{2}t+a^{2}/t)/2},} where | t | < 1. {\displaystyle |t|<1.} Using the two Neumann series representations, we can obtain the following symmetry relation for positive integral ν = n {\displaystyle \nu =n} Q n ( a , b ) + Q n ( b , a ) = 1 + e − ( a 2 + b 2 ) / 2 [ I 0 ( a b ) + ∑ k = 1 n − 1 a 2 k + b 2 k ( a b ) k I k ( a b ) ] . {\displaystyle Q_{n}(a,b)+Q_{n}(b,a)=1+e^{-(a^{2}+b^{2})/2}\left[I_{0}(ab)+\sum _{k=1}^{n-1}{\frac {a^{2k}+b^{2k}}{(ab)^{k}}}I_{k}(ab)\right].} In particular, for n = 1 {\displaystyle n=1} we have Q 1 ( a , b ) + Q 1 ( b , a ) = 1 + e − ( a 2 + b 2 ) / 2 I 0 ( a b ) . {\displaystyle Q_{1}(a,b)+Q_{1}(b,a)=1+e^{-(a^{2}+b^{2})/2}I_{0}(ab).} Some specific values of Marcum-Q function are[ 6]
Q ν ( 0 , 0 ) = 1 , {\displaystyle Q_{\nu }(0,0)=1,} Q ν ( a , 0 ) = 1 , {\displaystyle Q_{\nu }(a,0)=1,} Q ν ( a , + ∞ ) = 0 , {\displaystyle Q_{\nu }(a,+\infty )=0,} Q ν ( 0 , b ) = Γ ( ν , b 2 / 2 ) Γ ( ν ) , {\displaystyle Q_{\nu }(0,b)={\frac {\Gamma (\nu ,b^{2}/2)}{\Gamma (\nu )}},} Q ν ( + ∞ , b ) = 1 , {\displaystyle Q_{\nu }(+\infty ,b)=1,} Q ∞ ( a , b ) = 1 , {\displaystyle Q_{\infty }(a,b)=1,} For a = b {\displaystyle a=b} , by subtracting the two forms of Neumann series representations, we have[ 10] Q 1 ( a , a ) = 1 2 [ 1 + e − a 2 I 0 ( a 2 ) ] , {\displaystyle Q_{1}(a,a)={\frac {1}{2}}[1+e^{-a^{2}}I_{0}(a^{2})],} which when combined with the recursive formula gives Q n ( a , a ) = 1 2 [ 1 + e − a 2 I 0 ( a 2 ) ] + e − a 2 ∑ k = 1 n − 1 I k ( a 2 ) , {\displaystyle Q_{n}(a,a)={\frac {1}{2}}[1+e^{-a^{2}}I_{0}(a^{2})]+e^{-a^{2}}\sum _{k=1}^{n-1}I_{k}(a^{2}),} Q − n ( a , a ) = 1 2 [ 1 + e − a 2 I 0 ( a 2 ) ] − e − a 2 ∑ k = 1 n I k ( a 2 ) , {\displaystyle Q_{-n}(a,a)={\frac {1}{2}}[1+e^{-a^{2}}I_{0}(a^{2})]-e^{-a^{2}}\sum _{k=1}^{n}I_{k}(a^{2}),} for any non-negative integer n {\displaystyle n} . For ν = 1 / 2 {\displaystyle \nu =1/2} , using the basic integral definition of generalized Marcum Q-function, we have[ 8] [ 10] Q 1 / 2 ( a , b ) = 1 2 [ e r f c ( b − a 2 ) + e r f c ( b + a 2 ) ] . {\displaystyle Q_{1/2}(a,b)={\frac {1}{2}}\left[\mathrm {erfc} \left({\frac {b-a}{\sqrt {2}}}\right)+\mathrm {erfc} \left({\frac {b+a}{\sqrt {2}}}\right)\right].} For ν = 3 / 2 {\displaystyle \nu =3/2} , we have Q 3 / 2 ( a , b ) = Q 1 / 2 ( a , b ) + 2 π sinh ( a b ) a e − ( a 2 + b 2 ) / 2 . {\displaystyle Q_{3/2}(a,b)=Q_{1/2}(a,b)+{\sqrt {\frac {2}{\pi }}}\,{\frac {\sinh(ab)}{a}}e^{-(a^{2}+b^{2})/2}.} For ν = 5 / 2 {\displaystyle \nu =5/2} we have Q 5 / 2 ( a , b ) = Q 3 / 2 ( a , b ) + 2 π a b cosh ( a b ) − sinh ( a b ) a 3 e − ( a 2 + b 2 ) / 2 . {\displaystyle Q_{5/2}(a,b)=Q_{3/2}(a,b)+{\sqrt {\frac {2}{\pi }}}\,{\frac {ab\cosh(ab)-\sinh(ab)}{a^{3}}}e^{-(a^{2}+b^{2})/2}.} Assuming ν {\displaystyle \nu } to be fixed and a b {\displaystyle ab} large, let ζ = a / b > 0 {\displaystyle \zeta =a/b>0} , then the generalized Marcum-Q function has the following asymptotic form[ 7] Q ν ( a , b ) ∼ ∑ n = 0 ∞ ψ n , {\displaystyle Q_{\nu }(a,b)\sim \sum _{n=0}^{\infty }\psi _{n},} where ψ n {\displaystyle \psi _{n}} is given by ψ n = 1 2 ζ ν 2 π ( − 1 ) n [ A n ( ν − 1 ) − ζ A n ( ν ) ] ϕ n . {\displaystyle \psi _{n}={\frac {1}{2\zeta ^{\nu }{\sqrt {2\pi }}}}(-1)^{n}\left[A_{n}(\nu -1)-\zeta A_{n}(\nu )\right]\phi _{n}.} The functions ϕ n {\displaystyle \phi _{n}} and A n {\displaystyle A_{n}} are given by ϕ n = [ ( b − a ) 2 2 a b ] n − 1 2 Γ ( 1 2 − n , ( b − a ) 2 2 ) , {\displaystyle \phi _{n}=\left[{\frac {(b-a)^{2}}{2ab}}\right]^{n-{\frac {1}{2}}}\Gamma \left({\frac {1}{2}}-n,{\frac {(b-a)^{2}}{2}}\right),} A n ( ν ) = 2 − n Γ ( 1 2 + ν + n ) n ! Γ ( 1 2 + ν − n ) . {\displaystyle A_{n}(\nu )={\frac {2^{-n}\Gamma ({\frac {1}{2}}+\nu +n)}{n!\Gamma ({\frac {1}{2}}+\nu -n)}}.} The function A n ( ν ) {\displaystyle A_{n}(\nu )} satisfies the recursion A n + 1 ( ν ) = − ( 2 n + 1 ) 2 − 4 ν 2 8 ( n + 1 ) A n ( ν ) , {\displaystyle A_{n+1}(\nu )=-{\frac {(2n+1)^{2}-4\nu ^{2}}{8(n+1)}}A_{n}(\nu ),} for n ≥ 0 {\displaystyle n\geq 0} and A 0 ( ν ) = 1. {\displaystyle A_{0}(\nu )=1.} In the first term of the above asymptotic approximation, we have ϕ 0 = 2 π a b b − a e r f c ( b − a 2 ) . {\displaystyle \phi _{0}={\frac {\sqrt {2\pi ab}}{b-a}}\mathrm {erfc} \left({\frac {b-a}{\sqrt {2}}}\right).} Hence, assuming b > a {\displaystyle b>a} , the first term asymptotic approximation of the generalized Marcum-Q function is[ 7] Q ν ( a , b ) ∼ ψ 0 = ( b a ) ν − 1 2 Q ( b − a ) , {\displaystyle Q_{\nu }(a,b)\sim \psi _{0}=\left({\frac {b}{a}}\right)^{\nu -{\frac {1}{2}}}Q(b-a),} where Q ( ⋅ ) {\displaystyle Q(\cdot )} is the Gaussian Q-function . Here Q ν ( a , b ) ∼ 0.5 {\displaystyle Q_{\nu }(a,b)\sim 0.5} as a ↑ b . {\displaystyle a\uparrow b.} For the case when a > b {\displaystyle a>b} , we have[ 7] Q ν ( a , b ) ∼ 1 − ψ 0 = 1 − ( b a ) ν − 1 2 Q ( a − b ) . {\displaystyle Q_{\nu }(a,b)\sim 1-\psi _{0}=1-\left({\frac {b}{a}}\right)^{\nu -{\frac {1}{2}}}Q(a-b).} Here too Q ν ( a , b ) ∼ 0.5 {\displaystyle Q_{\nu }(a,b)\sim 0.5} as a ↓ b . {\displaystyle a\downarrow b.} The partial derivative of Q ν ( a , b ) {\displaystyle Q_{\nu }(a,b)} with respect to a {\displaystyle a} and b {\displaystyle b} is given by[ 12] [ 13] ∂ ∂ a Q ν ( a , b ) = a [ Q ν + 1 ( a , b ) − Q ν ( a , b ) ] = a ( b a ) ν e − ( a 2 + b 2 ) / 2 I ν ( a b ) , {\displaystyle {\frac {\partial }{\partial a}}Q_{\nu }(a,b)=a\left[Q_{\nu +1}(a,b)-Q_{\nu }(a,b)\right]=a\left({\frac {b}{a}}\right)^{\nu }e^{-(a^{2}+b^{2})/2}I_{\nu }(ab),} ∂ ∂ b Q ν ( a , b ) = b [ Q ν − 1 ( a , b ) − Q ν ( a , b ) ] = − b ( b a ) ν − 1 e − ( a 2 + b 2 ) / 2 I ν − 1 ( a b ) . {\displaystyle {\frac {\partial }{\partial b}}Q_{\nu }(a,b)=b\left[Q_{\nu -1}(a,b)-Q_{\nu }(a,b)\right]=-b\left({\frac {b}{a}}\right)^{\nu -1}e^{-(a^{2}+b^{2})/2}I_{\nu -1}(ab).} We can relate the two partial derivatives as 1 a ∂ ∂ a Q ν ( a , b ) + 1 b ∂ ∂ b Q ν + 1 ( a , b ) = 0. {\displaystyle {\frac {1}{a}}{\frac {\partial }{\partial a}}Q_{\nu }(a,b)+{\frac {1}{b}}{\frac {\partial }{\partial b}}Q_{\nu +1}(a,b)=0.} The n -th partial derivative of Q ν ( a , b ) {\displaystyle Q_{\nu }(a,b)} with respect to its arguments is given by[ 10] ∂ n ∂ a n Q ν ( a , b ) = n ! ( − a ) n ∑ k = 0 [ n / 2 ] ( − 2 a 2 ) − k k ! ( n − 2 k ) ! ∑ p = 0 n − k ( − 1 ) p ( n − k p ) Q ν + p ( a , b ) , {\displaystyle {\frac {\partial ^{n}}{\partial a^{n}}}Q_{\nu }(a,b)=n!(-a)^{n}\sum _{k=0}^{[n/2]}{\frac {(-2a^{2})^{-k}}{k!(n-2k)!}}\sum _{p=0}^{n-k}(-1)^{p}{\binom {n-k}{p}}Q_{\nu +p}(a,b),} ∂ n ∂ b n Q ν ( a , b ) = n ! a 1 − ν 2 n b n − ν + 1 e − ( a 2 + b 2 ) / 2 ∑ k = [ n / 2 ] n ( − 2 b 2 ) k ( n − k ) ! ( 2 k − n ) ! ∑ p = 0 k − 1 ( k − 1 p ) ( − a b ) p I ν − p − 1 ( a b ) . {\displaystyle {\frac {\partial ^{n}}{\partial b^{n}}}Q_{\nu }(a,b)={\frac {n!a^{1-\nu }}{2^{n}b^{n-\nu +1}}}e^{-(a^{2}+b^{2})/2}\sum _{k=[n/2]}^{n}{\frac {(-2b^{2})^{k}}{(n-k)!(2k-n)!}}\sum _{p=0}^{k-1}{\binom {k-1}{p}}\left(-{\frac {a}{b}}\right)^{p}I_{\nu -p-1}(ab).} Q ν 2 ( a , b ) > Q ν − 1 ( a , b ) + Q ν + 1 ( a , b ) 2 > Q ν − 1 ( a , b ) Q ν + 1 ( a , b ) {\displaystyle Q_{\nu }^{2}(a,b)>{\frac {Q_{\nu -1}(a,b)+Q_{\nu +1}(a,b)}{2}}>Q_{\nu -1}(a,b)Q_{\nu +1}(a,b)} for all a ≥ b > 0 {\displaystyle a\geq b>0} and ν > 1 {\displaystyle \nu >1} . Based on monotonicity and log-concavity [ edit ] Various upper and lower bounds of generalized Marcum-Q function can be obtained using monotonicity and log-concavity of the function ν ↦ Q ν ( a , b ) {\displaystyle \nu \mapsto Q_{\nu }(a,b)} and the fact that we have closed form expression for Q ν ( a , b ) {\displaystyle Q_{\nu }(a,b)} when ν {\displaystyle \nu } is half-integer valued.
Let ⌊ x ⌋ 0.5 {\displaystyle \lfloor x\rfloor _{0.5}} and ⌈ x ⌉ 0.5 {\displaystyle \lceil x\rceil _{0.5}} denote the pair of half-integer rounding operators that map a real x {\displaystyle x} to its nearest left and right half-odd integer, respectively, according to the relations
⌊ x ⌋ 0.5 = ⌊ x − 0.5 ⌋ + 0.5 {\displaystyle \lfloor x\rfloor _{0.5}=\lfloor x-0.5\rfloor +0.5} ⌈ x ⌉ 0.5 = ⌈ x + 0.5 ⌉ − 0.5 {\displaystyle \lceil x\rceil _{0.5}=\lceil x+0.5\rceil -0.5} where ⌊ x ⌋ {\displaystyle \lfloor x\rfloor } and ⌈ x ⌉ {\displaystyle \lceil x\rceil } denote the integer floor and ceiling functions.
The monotonicity of the function ν ↦ Q ν ( a , b ) {\displaystyle \nu \mapsto Q_{\nu }(a,b)} for all a ≥ 0 {\displaystyle a\geq 0} and b > 0 {\displaystyle b>0} gives us the following simple bound[ 14] [ 8] [ 15] Q ⌊ ν ⌋ 0.5 ( a , b ) < Q ν ( a , b ) < Q ⌈ ν ⌉ 0.5 ( a , b ) . {\displaystyle Q_{\lfloor \nu \rfloor _{0.5}}(a,b)<Q_{\nu }(a,b)<Q_{\lceil \nu \rceil _{0.5}}(a,b).} However, the relative error of this bound does not tend to zero when b → ∞ {\displaystyle b\to \infty } .[ 5] For integral values of ν = n {\displaystyle \nu =n} , this bound reduces to Q n − 0.5 ( a , b ) < Q n ( a , b ) < Q n + 0.5 ( a , b ) . {\displaystyle Q_{n-0.5}(a,b)<Q_{n}(a,b)<Q_{n+0.5}(a,b).} A very good approximation of the generalized Marcum Q-function for integer valued ν = n {\displaystyle \nu =n} is obtained by taking the arithmetic mean of the upper and lower bound[ 15] Q n ( a , b ) ≈ Q n − 0.5 ( a , b ) + Q n + 0.5 ( a , b ) 2 . {\displaystyle Q_{n}(a,b)\approx {\frac {Q_{n-0.5}(a,b)+Q_{n+0.5}(a,b)}{2}}.} A tighter bound can be obtained by exploiting the log-concavity of ν ↦ Q ν ( a , b ) {\displaystyle \nu \mapsto Q_{\nu }(a,b)} on [ 1 , ∞ ) {\displaystyle [1,\infty )} as[ 5] Q ν 1 ( a , b ) ν 2 − v Q ν 2 ( a , b ) v − ν 1 < Q ν ( a , b ) < Q ν 2 ( a , b ) ν 2 − ν + 1 Q ν 2 + 1 ( a , b ) ν 2 − ν , {\displaystyle Q_{\nu _{1}}(a,b)^{\nu _{2}-v}Q_{\nu _{2}}(a,b)^{v-\nu _{1}}<Q_{\nu }(a,b)<{\frac {Q_{\nu _{2}}(a,b)^{\nu _{2}-\nu +1}}{Q_{\nu _{2}+1}(a,b)^{\nu _{2}-\nu }}},} where ν 1 = ⌊ ν ⌋ 0.5 {\displaystyle \nu _{1}=\lfloor \nu \rfloor _{0.5}} and ν 2 = ⌈ ν ⌉ 0.5 {\displaystyle \nu _{2}=\lceil \nu \rceil _{0.5}} for ν ≥ 1.5 {\displaystyle \nu \geq 1.5} . The tightness of this bound improves as either a {\displaystyle a} or ν {\displaystyle \nu } increases. The relative error of this bound converges to 0 as b → ∞ {\displaystyle b\to \infty } .[ 5] For integral values of ν = n {\displaystyle \nu =n} , this bound reduces to Q n − 0.5 ( a , b ) Q n + 0.5 ( a , b ) < Q n ( a , b ) < Q n + 0.5 ( a , b ) Q n + 0.5 ( a , b ) Q n + 1.5 ( a , b ) . {\displaystyle {\sqrt {Q_{n-0.5}(a,b)Q_{n+0.5}(a,b)}}<Q_{n}(a,b)<Q_{n+0.5}(a,b){\sqrt {\frac {Q_{n+0.5}(a,b)}{Q_{n+1.5}(a,b)}}}.} Cauchy-Schwarz bound [ edit ] Using the trigonometric integral representation for integer valued ν = n {\displaystyle \nu =n} , the following Cauchy-Schwarz bound can be obtained[ 3]
e − b 2 / 2 ≤ Q n ( a , b ) ≤ exp [ − 1 2 ( b 2 + a 2 ) ] I 0 ( 2 a b ) 2 n − 1 2 + ζ 2 ( 1 − n ) 2 ( 1 − ζ 2 ) , ζ < 1 , {\displaystyle e^{-b^{2}/2}\leq Q_{n}(a,b)\leq \exp \left[-{\frac {1}{2}}(b^{2}+a^{2})\right]{\sqrt {I_{0}(2ab)}}{\sqrt {{\frac {2n-1}{2}}+{\frac {\zeta ^{2(1-n)}}{2(1-\zeta ^{2})}}}},\qquad \zeta <1,} 1 − Q n ( a , b ) ≤ exp [ − 1 2 ( b 2 + a 2 ) ] I 0 ( 2 a b ) ζ 2 ( 1 − n ) 2 ( ζ 2 − 1 ) , ζ > 1 , {\displaystyle 1-Q_{n}(a,b)\leq \exp \left[-{\frac {1}{2}}(b^{2}+a^{2})\right]{\sqrt {I_{0}(2ab)}}{\sqrt {\frac {\zeta ^{2(1-n)}}{2(\zeta ^{2}-1)}}},\qquad \zeta >1,} where ζ = a / b > 0 {\displaystyle \zeta =a/b>0} .
Exponential-type bounds [ edit ] For analytical purpose, it is often useful to have bounds in simple exponential form, even though they may not be the tightest bounds achievable. Letting ζ = a / b > 0 {\displaystyle \zeta =a/b>0} , one such bound for integer valued ν = n {\displaystyle \nu =n} is given as[ 16] [ 3]
e − ( b + a ) 2 / 2 ≤ Q n ( a , b ) ≤ e − ( b − a ) 2 / 2 + ζ 1 − n − 1 π ( 1 − ζ ) [ e − ( b − a ) 2 / 2 − e − ( b + a ) 2 / 2 ] , ζ < 1 , {\displaystyle e^{-(b+a)^{2}/2}\leq Q_{n}(a,b)\leq e^{-(b-a)^{2}/2}+{\frac {\zeta ^{1-n}-1}{\pi (1-\zeta )}}\left[e^{-(b-a)^{2}/2}-e^{-(b+a)^{2}/2}\right],\qquad \zeta <1,} Q n ( a , b ) ≥ 1 − 1 2 [ e − ( a − b ) 2 / 2 − e − ( a + b ) 2 / 2 ] , ζ > 1. {\displaystyle Q_{n}(a,b)\geq 1-{\frac {1}{2}}\left[e^{-(a-b)^{2}/2}-e^{-(a+b)^{2}/2}\right],\qquad \zeta >1.} When n = 1 {\displaystyle n=1} , the bound simplifies to give
e − ( b + a ) 2 / 2 ≤ Q 1 ( a , b ) ≤ e − ( b − a ) 2 / 2 , ζ < 1 , {\displaystyle e^{-(b+a)^{2}/2}\leq Q_{1}(a,b)\leq e^{-(b-a)^{2}/2},\qquad \zeta <1,} 1 − 1 2 [ e − ( a − b ) 2 / 2 − e − ( a + b ) 2 / 2 ] ≤ Q 1 ( a , b ) , ζ > 1. {\displaystyle 1-{\frac {1}{2}}\left[e^{-(a-b)^{2}/2}-e^{-(a+b)^{2}/2}\right]\leq Q_{1}(a,b),\qquad \zeta >1.} Another such bound obtained via Cauchy-Schwarz inequality is given as[ 3]
e − b 2 / 2 ≤ Q n ( a , b ) ≤ 1 2 2 n − 1 2 + ζ 2 ( 1 − n ) 2 ( 1 − ζ 2 ) [ e − ( b − a ) 2 / 2 + e − ( b + a ) 2 / 2 ] , ζ < 1 {\displaystyle e^{-b^{2}/2}\leq Q_{n}(a,b)\leq {\frac {1}{2}}{\sqrt {{\frac {2n-1}{2}}+{\frac {\zeta ^{2(1-n)}}{2(1-\zeta ^{2})}}}}\left[e^{-(b-a)^{2}/2}+e^{-(b+a)^{2}/2}\right],\qquad \zeta <1} Q n ( a , b ) ≥ 1 − 1 2 ζ 2 ( 1 − n ) 2 ( ζ 2 − 1 ) [ e − ( b − a ) 2 / 2 + e − ( b + a ) 2 / 2 ] , ζ > 1. {\displaystyle Q_{n}(a,b)\geq 1-{\frac {1}{2}}{\sqrt {\frac {\zeta ^{2(1-n)}}{2(\zeta ^{2}-1)}}}\left[e^{-(b-a)^{2}/2}+e^{-(b+a)^{2}/2}\right],\qquad \zeta >1.} Chernoff-type bound [ edit ] Chernoff-type bounds for the generalized Marcum Q-function, where ν = n {\displaystyle \nu =n} is an integer, is given by[ 16] [ 3]
( 1 − 2 λ ) − n exp ( − λ b 2 + λ n a 2 1 − 2 λ ) ≥ { Q n ( a , b ) , b 2 > n ( a 2 + 2 ) 1 − Q n ( a , b ) , b 2 < n ( a 2 + 2 ) {\displaystyle (1-2\lambda )^{-n}\exp \left(-\lambda b^{2}+{\frac {\lambda na^{2}}{1-2\lambda }}\right)\geq \left\{{\begin{array}{lr}Q_{n}(a,b),&b^{2}>n(a^{2}+2)\\1-Q_{n}(a,b),&b^{2}<n(a^{2}+2)\end{array}}\right.} where the Chernoff parameter ( 0 < λ < 1 / 2 ) {\displaystyle (0<\lambda <1/2)} has optimum value λ 0 {\displaystyle \lambda _{0}} of
λ 0 = 1 2 ( 1 − n b 2 − n b 2 1 + ( a b ) 2 n ) . {\displaystyle \lambda _{0}={\frac {1}{2}}\left(1-{\frac {n}{b^{2}}}-{\frac {n}{b^{2}}}{\sqrt {1+{\frac {(ab)^{2}}{n}}}}\right).} Semi-linear approximation [ edit ] The first-order Marcum-Q function can be semi-linearly approximated by [ 17]
Q 1 ( a , b ) = { 1 , i f b < c 1 − β 0 e − 1 2 ( a 2 + ( β 0 ) 2 ) I 0 ( a β 0 ) ( b − β 0 ) + Q 1 ( a , β 0 ) , i f c 1 ≤ b ≤ c 2 0 , i f b > c 2 {\displaystyle {\begin{aligned}Q_{1}(a,b)={\begin{cases}1,~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\mathrm {if} ~b<c_{1}\\-\beta _{0}e^{-{\frac {1}{2}}\left(a^{2}+\left(\beta _{0}\right)^{2}\right)}I_{0}\left(a\beta _{0}\right)\left(b-\beta _{0}\right)+Q_{1}\left(a,\beta _{0}\right),~~~~~\mathrm {if} ~c_{1}\leq b\leq c_{2}\\0,~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\mathrm {if} ~b>c_{2}\end{cases}}\end{aligned}}} where
β 0 = a + a 2 + 2 2 , {\displaystyle {\begin{aligned}\beta _{0}={\frac {a+{\sqrt {a^{2}+2}}}{2}},\end{aligned}}} c 1 ( a ) = max ( 0 , β 0 + Q 1 ( a , β 0 ) − 1 β 0 e − 1 2 ( a 2 + ( β 0 ) 2 ) I 0 ( a β 0 ) ) , {\displaystyle {\begin{aligned}c_{1}(a)=\max {\Bigg (}0,\beta _{0}+{\frac {Q_{1}\left(a,\beta _{0}\right)-1}{\beta _{0}e^{-{\frac {1}{2}}\left(a^{2}+\left(\beta _{0}\right)^{2}\right)}I_{0}\left(a\beta _{0}\right)}}{\Bigg )},\end{aligned}}} and
c 2 ( a ) = β 0 + Q 1 ( a , β 0 ) β 0 e − 1 2 ( a 2 + ( β 0 ) 2 ) I 0 ( a β 0 ) . {\displaystyle {\begin{aligned}c_{2}(a)=\beta _{0}+{\frac {Q_{1}\left(a,\beta _{0}\right)}{\beta _{0}e^{-{\frac {1}{2}}\left(a^{2}+\left(\beta _{0}\right)^{2}\right)}I_{0}\left(a\beta _{0}\right)}}.\end{aligned}}} It is convenient to re-express the Marcum Q-function as[ 18]
P N ( X , Y ) = Q N ( 2 N X , 2 Y ) . {\displaystyle P_{N}(X,Y)=Q_{N}({\sqrt {2NX}},{\sqrt {2Y}}).} The P N ( X , Y ) {\displaystyle P_{N}(X,Y)} can be interpreted as the detection probability of N {\displaystyle N} incoherently integrated received signal samples of constant received signal-to-noise ratio, X {\displaystyle X} , with a normalized detection threshold Y {\displaystyle Y} . In this equivalent form of Marcum Q-function, for given a {\displaystyle a} and b {\displaystyle b} , we have X = a 2 / 2 N {\displaystyle X=a^{2}/2N} and Y = b 2 / 2 {\displaystyle Y=b^{2}/2} . Many expressions exist that can represent P N ( X , Y ) {\displaystyle P_{N}(X,Y)} . However, the five most reliable, accurate, and efficient ones for numerical computation are given below. They are form one:[ 18]
P N ( X , Y ) = ∑ k = 0 ∞ e − N X ( N X ) k k ! ∑ m = 0 N − 1 + k e − Y Y m m ! , {\displaystyle P_{N}(X,Y)=\sum _{k=0}^{\infty }e^{-NX}{\frac {(NX)^{k}}{k!}}\sum _{m=0}^{N-1+k}e^{-Y}{\frac {Y^{m}}{m!}},} form two:[ 18]
P N ( X , Y ) = ∑ m = 0 N − 1 e − Y Y m m ! + ∑ m = N ∞ e − Y Y m m ! ( 1 − ∑ k = 0 m − N e − N X ( N X ) k k ! ) , {\displaystyle P_{N}(X,Y)=\sum _{m=0}^{N-1}e^{-Y}{\frac {Y^{m}}{m!}}+\sum _{m=N}^{\infty }e^{-Y}{\frac {Y^{m}}{m!}}\left(1-\sum _{k=0}^{m-N}e^{-NX}{\frac {(NX)^{k}}{k!}}\right),} form three:[ 18]
1 − P N ( X , Y ) = ∑ m = N ∞ e − Y Y m m ! ∑ k = 0 m − N e − N X ( N X ) k k ! , {\displaystyle 1-P_{N}(X,Y)=\sum _{m=N}^{\infty }e^{-Y}{\frac {Y^{m}}{m!}}\sum _{k=0}^{m-N}e^{-NX}{\frac {(NX)^{k}}{k!}},} form four:[ 18]
1 − P N ( X , Y ) = ∑ k = 0 ∞ e − N X ( N X ) k k ! ( 1 − ∑ m = 0 N − 1 + k e − Y Y m m ! ) , {\displaystyle 1-P_{N}(X,Y)=\sum _{k=0}^{\infty }e^{-NX}{\frac {(NX)^{k}}{k!}}\left(1-\sum _{m=0}^{N-1+k}e^{-Y}{\frac {Y^{m}}{m!}}\right),} and form five:[ 18]
1 − P N ( X , Y ) = e − ( N X + Y ) ∑ r = N ∞ ( Y N X ) r / 2 I r ( 2 N X Y ) . {\displaystyle 1-P_{N}(X,Y)=e^{-(NX+Y)}\sum _{r=N}^{\infty }\left({\frac {Y}{NX}}\right)^{r/2}I_{r}(2{\sqrt {NXY}}).} Among these five form, the second form is the most robust.[ 18]
The generalized Marcum Q-function can be used to represent the cumulative distribution function (cdf) of many random variables:
If X ∼ E x p ( λ ) {\displaystyle X\sim \mathrm {Exp} (\lambda )} is an exponential distribution with rate parameter λ {\displaystyle \lambda } , then its cdf is given by F X ( x ) = 1 − Q 1 ( 0 , 2 λ x ) {\displaystyle F_{X}(x)=1-Q_{1}\left(0,{\sqrt {2\lambda x}}\right)} If X ∼ E r l a n g ( k , λ ) {\displaystyle X\sim \mathrm {Erlang} (k,\lambda )} is a Erlang distribution with shape parameter k {\displaystyle k} and rate parameter λ {\displaystyle \lambda } , then its cdf is given by F X ( x ) = 1 − Q k ( 0 , 2 λ x ) {\displaystyle F_{X}(x)=1-Q_{k}\left(0,{\sqrt {2\lambda x}}\right)} If X ∼ χ k 2 {\displaystyle X\sim \chi _{k}^{2}} is a chi-squared distribution with k {\displaystyle k} degrees of freedom, then its cdf is given by F X ( x ) = 1 − Q k / 2 ( 0 , x ) {\displaystyle F_{X}(x)=1-Q_{k/2}(0,{\sqrt {x}})} If X ∼ G a m m a ( α , β ) {\displaystyle X\sim \mathrm {Gamma} (\alpha ,\beta )} is a gamma distribution with shape parameter α {\displaystyle \alpha } and rate parameter β {\displaystyle \beta } , then its cdf is given by F X ( x ) = 1 − Q α ( 0 , 2 β x ) {\displaystyle F_{X}(x)=1-Q_{\alpha }(0,{\sqrt {2\beta x}})} If X ∼ W e i b u l l ( k , λ ) {\displaystyle X\sim \mathrm {Weibull} (k,\lambda )} is a Weibull distribution with shape parameters k {\displaystyle k} and scale parameter λ {\displaystyle \lambda } , then its cdf is given by F X ( x ) = 1 − Q 1 ( 0 , 2 ( x λ ) k 2 ) {\displaystyle F_{X}(x)=1-Q_{1}\left(0,{\sqrt {2}}\left({\frac {x}{\lambda }}\right)^{\frac {k}{2}}\right)} If X ∼ G G ( a , d , p ) {\displaystyle X\sim \mathrm {GG} (a,d,p)} is a generalized gamma distribution with parameters a , d , p {\displaystyle a,d,p} , then its cdf is given by F X ( x ) = 1 − Q d p ( 0 , 2 ( x a ) p 2 ) {\displaystyle F_{X}(x)=1-Q_{\frac {d}{p}}\left(0,{\sqrt {2}}\left({\frac {x}{a}}\right)^{\frac {p}{2}}\right)} If X ∼ χ k 2 ( λ ) {\displaystyle X\sim \chi _{k}^{2}(\lambda )} is a non-central chi-squared distribution with non-centrality parameter λ {\displaystyle \lambda } and k {\displaystyle k} degrees of freedom, then its cdf is given by F X ( x ) = 1 − Q k / 2 ( λ , x ) {\displaystyle F_{X}(x)=1-Q_{k/2}({\sqrt {\lambda }},{\sqrt {x}})} If X ∼ R a y l e i g h ( σ ) {\displaystyle X\sim \mathrm {Rayleigh} (\sigma )} is a Rayleigh distribution with parameter σ {\displaystyle \sigma } , then its cdf is given by F X ( x ) = 1 − Q 1 ( 0 , x σ ) {\displaystyle F_{X}(x)=1-Q_{1}\left(0,{\frac {x}{\sigma }}\right)} If X ∼ M a x w e l l ( σ ) {\displaystyle X\sim \mathrm {Maxwell} (\sigma )} is a Maxwell–Boltzmann distribution with parameter σ {\displaystyle \sigma } , then its cdf is given by F X ( x ) = 1 − Q 3 / 2 ( 0 , x σ ) {\displaystyle F_{X}(x)=1-Q_{3/2}\left(0,{\frac {x}{\sigma }}\right)} If X ∼ χ k {\displaystyle X\sim \chi _{k}} is a chi distribution with k {\displaystyle k} degrees of freedom, then its cdf is given by F X ( x ) = 1 − Q k / 2 ( 0 , x ) {\displaystyle F_{X}(x)=1-Q_{k/2}(0,x)} If X ∼ N a k a g a m i ( m , Ω ) {\displaystyle X\sim \mathrm {Nakagami} (m,\Omega )} is a Nakagami distribution with m {\displaystyle m} as shape parameter and Ω {\displaystyle \Omega } as spread parameter, then its cdf is given by F X ( x ) = 1 − Q m ( 0 , 2 m Ω x ) {\displaystyle F_{X}(x)=1-Q_{m}\left(0,{\sqrt {\frac {2m}{\Omega }}}x\right)} If X ∼ R i c e ( ν , σ ) {\displaystyle X\sim \mathrm {Rice} (\nu ,\sigma )} is a Rice distribution with parameters ν {\displaystyle \nu } and σ {\displaystyle \sigma } , then its cdf is given by F X ( x ) = 1 − Q 1 ( ν σ , x σ ) {\displaystyle F_{X}(x)=1-Q_{1}\left({\frac {\nu }{\sigma }},{\frac {x}{\sigma }}\right)} If X ∼ χ k ( λ ) {\displaystyle X\sim \chi _{k}(\lambda )} is a non-central chi distribution with non-centrality parameter λ {\displaystyle \lambda } and k {\displaystyle k} degrees of freedom, then its cdf is given by F X ( x ) = 1 − Q k / 2 ( λ , x ) {\displaystyle F_{X}(x)=1-Q_{k/2}(\lambda ,x)} ^ J.I. 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