Generalisation of the generalised hypergeometric function pFq(z)
In mathematics , the Fox–Wright function (also known as Fox–Wright Psi function , not to be confused with Wright Omega function ) is a generalisation of the generalised hypergeometric function p F q (z ) based on ideas of Charles Fox (1928 ) and E. Maitland Wright (1935 ):
p
Ψ
q
[
(
a
1
,
A
1
)
(
a
2
,
A
2
)
…
(
a
p
,
A
p
)
(
b
1
,
B
1
)
(
b
2
,
B
2
)
…
(
b
q
,
B
q
)
;
z
]
=
∑
n
=
0
∞
Γ
(
a
1
+
A
1
n
)
⋯
Γ
(
a
p
+
A
p
n
)
Γ
(
b
1
+
B
1
n
)
⋯
Γ
(
b
q
+
B
q
n
)
z
n
n
!
.
{\displaystyle {}_{p}\Psi _{q}\left[{\begin{matrix}(a_{1},A_{1})&(a_{2},A_{2})&\ldots &(a_{p},A_{p})\\(b_{1},B_{1})&(b_{2},B_{2})&\ldots &(b_{q},B_{q})\end{matrix}};z\right]=\sum _{n=0}^{\infty }{\frac {\Gamma (a_{1}+A_{1}n)\cdots \Gamma (a_{p}+A_{p}n)}{\Gamma (b_{1}+B_{1}n)\cdots \Gamma (b_{q}+B_{q}n)}}\,{\frac {z^{n}}{n!}}.}
Upon changing the normalisation
p
Ψ
q
∗
[
(
a
1
,
A
1
)
(
a
2
,
A
2
)
…
(
a
p
,
A
p
)
(
b
1
,
B
1
)
(
b
2
,
B
2
)
…
(
b
q
,
B
q
)
;
z
]
=
Γ
(
b
1
)
⋯
Γ
(
b
q
)
Γ
(
a
1
)
⋯
Γ
(
a
p
)
∑
n
=
0
∞
Γ
(
a
1
+
A
1
n
)
⋯
Γ
(
a
p
+
A
p
n
)
Γ
(
b
1
+
B
1
n
)
⋯
Γ
(
b
q
+
B
q
n
)
z
n
n
!
{\displaystyle {}_{p}\Psi _{q}^{*}\left[{\begin{matrix}(a_{1},A_{1})&(a_{2},A_{2})&\ldots &(a_{p},A_{p})\\(b_{1},B_{1})&(b_{2},B_{2})&\ldots &(b_{q},B_{q})\end{matrix}};z\right]={\frac {\Gamma (b_{1})\cdots \Gamma (b_{q})}{\Gamma (a_{1})\cdots \Gamma (a_{p})}}\sum _{n=0}^{\infty }{\frac {\Gamma (a_{1}+A_{1}n)\cdots \Gamma (a_{p}+A_{p}n)}{\Gamma (b_{1}+B_{1}n)\cdots \Gamma (b_{q}+B_{q}n)}}\,{\frac {z^{n}}{n!}}}
it becomes p F q (z ) for A 1...p = B 1...q = 1.
The Fox–Wright function is a special case of the Fox H-function (Srivastava & Manocha 1984 , p. 50):
p
Ψ
q
[
(
a
1
,
A
1
)
(
a
2
,
A
2
)
…
(
a
p
,
A
p
)
(
b
1
,
B
1
)
(
b
2
,
B
2
)
…
(
b
q
,
B
q
)
;
z
]
=
H
p
,
q
+
1
1
,
p
[
−
z
|
(
1
−
a
1
,
A
1
)
(
1
−
a
2
,
A
2
)
…
(
1
−
a
p
,
A
p
)
(
0
,
1
)
(
1
−
b
1
,
B
1
)
(
1
−
b
2
,
B
2
)
…
(
1
−
b
q
,
B
q
)
]
.
{\displaystyle {}_{p}\Psi _{q}\left[{\begin{matrix}(a_{1},A_{1})&(a_{2},A_{2})&\ldots &(a_{p},A_{p})\\(b_{1},B_{1})&(b_{2},B_{2})&\ldots &(b_{q},B_{q})\end{matrix}};z\right]=H_{p,q+1}^{1,p}\left[-z\left|{\begin{matrix}(1-a_{1},A_{1})&(1-a_{2},A_{2})&\ldots &(1-a_{p},A_{p})\\(0,1)&(1-b_{1},B_{1})&(1-b_{2},B_{2})&\ldots &(1-b_{q},B_{q})\end{matrix}}\right.\right].}
A special case of Fox–Wright function appears as a part of the normalizing constant of the modified half-normal distribution [ 1] with the pdf on
(
0
,
∞
)
{\displaystyle (0,\infty )}
is given as
f
(
x
)
=
2
β
α
2
x
α
−
1
exp
(
−
β
x
2
+
γ
x
)
Ψ
(
α
2
,
γ
β
)
{\displaystyle f(x)={\frac {2\beta ^{\frac {\alpha }{2}}x^{\alpha -1}\exp(-\beta x^{2}+\gamma x)}{\Psi {\left({\frac {\alpha }{2}},{\frac {\gamma }{\sqrt {\beta }}}\right)}}}}
, where
Ψ
(
α
,
z
)
=
1
Ψ
1
(
(
α
,
1
2
)
(
1
,
0
)
;
z
)
{\displaystyle \Psi (\alpha ,z)={}_{1}\Psi _{1}\left({\begin{matrix}\left(\alpha ,{\frac {1}{2}}\right)\\(1,0)\end{matrix}};z\right)}
denotes the Fox–Wright Psi function .
Wright function
The entire function
W
λ
,
μ
(
z
)
{\displaystyle W_{\lambda ,\mu }(z)}
is often called the Wright function .[ 2] It is the special case of
0
Ψ
1
[
…
]
{\displaystyle {}_{0}\Psi _{1}\left[\ldots \right]}
of the Fox–Wright function. Its series representation is
W
λ
,
μ
(
z
)
=
∑
n
=
0
∞
z
n
n
!
Γ
(
λ
n
+
μ
)
,
λ
>
−
1.
{\displaystyle W_{\lambda ,\mu }(z)=\sum _{n=0}^{\infty }{\frac {z^{n}}{n!\,\Gamma (\lambda n+\mu )}},\lambda >-1.}
This function is used extensively in fractional calculus and the stable count distribution . Recall that
lim
λ
→
0
W
λ
,
μ
(
z
)
=
e
z
/
Γ
(
μ
)
{\displaystyle \lim \limits _{\lambda \to 0}W_{\lambda ,\mu }(z)=e^{z}/\Gamma (\mu )}
. Hence, a non-zero
λ
{\displaystyle \lambda }
with zero
μ
{\displaystyle \mu }
is the simplest nontrivial extension of the exponential function in such context.
Three properties were stated in Theorem 1 of Wright (1933)[ 3] and 18.1(30–32) of Erdelyi, Bateman Project, Vol 3 (1955)[ 4] (p. 212)
λ
z
W
λ
,
μ
+
λ
(
z
)
=
W
λ
,
μ
−
1
(
z
)
+
(
1
−
μ
)
W
λ
,
μ
(
z
)
(
a
)
d
d
z
W
λ
,
μ
(
z
)
=
W
λ
,
μ
+
λ
(
z
)
(
b
)
λ
z
d
d
z
W
λ
,
μ
(
z
)
=
W
λ
,
μ
−
1
(
z
)
+
(
1
−
μ
)
W
λ
,
μ
(
z
)
(
c
)
{\displaystyle {\begin{aligned}\lambda zW_{\lambda ,\mu +\lambda }(z)&=W_{\lambda ,\mu -1}(z)+(1-\mu )W_{\lambda ,\mu }(z)&(a)\\[6pt]{d \over dz}W_{\lambda ,\mu }(z)&=W_{\lambda ,\mu +\lambda }(z)&(b)\\[6pt]\lambda z{d \over dz}W_{\lambda ,\mu }(z)&=W_{\lambda ,\mu -1}(z)+(1-\mu )W_{\lambda ,\mu }(z)&(c)\end{aligned}}}
Equation (a) is a recurrence formula. (b) and (c) provide two paths to reduce a derivative. And (c) can be derived from (a) and (b).
A special case of (c) is
λ
=
−
c
α
,
μ
=
0
{\displaystyle \lambda =-c\alpha ,\mu =0}
. Replacing
z
{\displaystyle z}
with
−
x
α
{\displaystyle -x^{\alpha }}
, we have
x
d
d
x
W
−
c
α
,
0
(
−
x
α
)
=
−
1
c
[
W
−
c
α
,
−
1
(
−
x
α
)
+
W
−
c
α
,
0
(
−
x
α
)
]
{\displaystyle {\begin{array}{lcl}x{d \over dx}W_{-c\alpha ,0}(-x^{\alpha })&=&-{\frac {1}{c}}\left[W_{-c\alpha ,-1}(-x^{\alpha })+W_{-c\alpha ,0}(-x^{\alpha })\right]\end{array}}}
A special case of (a) is
λ
=
−
α
,
μ
=
1
{\displaystyle \lambda =-\alpha ,\mu =1}
. Replacing
z
{\displaystyle z}
with
−
z
{\displaystyle -z}
, we have
α
z
W
−
α
,
1
−
α
(
−
z
)
=
W
−
α
,
0
(
−
z
)
{\displaystyle \alpha zW_{-\alpha ,1-\alpha }(-z)=W_{-\alpha ,0}(-z)}
Two notations,
M
α
(
z
)
{\displaystyle M_{\alpha }(z)}
and
F
α
(
z
)
{\displaystyle F_{\alpha }(z)}
, were used extensively in the literatures:
M
α
(
z
)
=
W
−
α
,
1
−
α
(
−
z
)
,
⟹
F
α
(
z
)
=
W
−
α
,
0
(
−
z
)
=
α
z
M
α
(
z
)
.
{\displaystyle {\begin{aligned}M_{\alpha }(z)&=W_{-\alpha ,1-\alpha }(-z),\\[1ex]\implies F_{\alpha }(z)&=W_{-\alpha ,0}(-z)=\alpha zM_{\alpha }(z).\end{aligned}}}
M-Wright function
M
α
(
z
)
{\displaystyle M_{\alpha }(z)}
is known as the M-Wright function, entering as a probability density in a relevant class of self-similar stochastic processes, generally referred to as time-fractional diffusion processes.
Its properties were surveyed in Mainardi et al (2010).[ 5]
Through the stable count distribution ,
α
{\displaystyle \alpha }
is connected to Lévy's stability index
(
0
<
α
≤
1
)
{\displaystyle (0<\alpha \leq 1)}
.
Its asymptotic expansion of
M
α
(
z
)
{\displaystyle M_{\alpha }(z)}
for
α
>
0
{\displaystyle \alpha >0}
is
M
α
(
r
α
)
=
A
(
α
)
r
(
α
−
1
/
2
)
/
(
1
−
α
)
e
−
B
(
α
)
r
1
/
(
1
−
α
)
,
r
→
∞
,
{\displaystyle M_{\alpha }\left({\frac {r}{\alpha }}\right)=A(\alpha )\,r^{(\alpha -1/2)/(1-\alpha )}\,e^{-B(\alpha )\,r^{1/(1-\alpha )}},\,\,r\rightarrow \infty ,}
where
A
(
α
)
=
1
2
π
(
1
−
α
)
,
{\displaystyle A(\alpha )={\frac {1}{\sqrt {2\pi (1-\alpha )}}},}
B
(
α
)
=
1
−
α
α
.
{\displaystyle B(\alpha )={\frac {1-\alpha }{\alpha }}.}
See also
Prabhakar function
Hypergeometric function
Generalized hypergeometric function
Modified half-normal distribution [ 1] with the pdf on
(
0
,
∞
)
{\displaystyle (0,\infty )}
is given as
f
(
x
)
=
2
β
α
2
x
α
−
1
exp
(
−
β
x
2
+
γ
x
)
Ψ
(
α
2
,
γ
β
)
{\displaystyle f(x)={\frac {2\beta ^{\frac {\alpha }{2}}x^{\alpha -1}\exp(-\beta x^{2}+\gamma x)}{\Psi {\left({\frac {\alpha }{2}},{\frac {\gamma }{\sqrt {\beta }}}\right)}}}}
, where
Ψ
(
α
,
z
)
=
1
Ψ
1
(
(
α
,
1
2
)
(
1
,
0
)
;
z
)
{\displaystyle \Psi (\alpha ,z)={}_{1}\Psi _{1}\left({\begin{matrix}\left(\alpha ,{\frac {1}{2}}\right)\\(1,0)\end{matrix}};z\right)}
denotes the Fox–Wright Psi function .
References
^ a b Sun, Jingchao; Kong, Maiying; Pal, Subhadip (22 June 2021). "The Modified-Half-Normal distribution: Properties and an efficient sampling scheme" . Communications in Statistics – Theory and Methods . 52 (5): 1591–1613. doi :10.1080/03610926.2021.1934700 . ISSN 0361-0926 . S2CID 237919587 .
^ Weisstein, Eric W. "Wright Function" . From MathWorld--A Wolfram Web Resource . Retrieved 2022-12-03 .
^ Wright, E. (1933). "On the Coefficients of Power Series Having Exponential Singularities". Journal of the London Mathematical Society . Second Series: 71–79. doi :10.1112/JLMS/S1-8.1.71 . S2CID 122652898 .
^ Erdelyi, A (1955). The Bateman Project, Volume 3 . California Institute of Technology.
^ Mainardi, Francesco; Mura, Antonio; Pagnini, Gianni (2010-04-17). The M-Wright function in time-fractional diffusion processes: a tutorial survey . arXiv :1004.2950 .
Fox, C. (1928). "The asymptotic expansion of integral functions defined by generalized hypergeometric series". Proc. London Math. Soc . 27 (1): 389–400. doi :10.1112/plms/s2-27.1.389 .
Wright, E. M. (1935). "The asymptotic expansion of the generalized hypergeometric function". J. London Math. Soc . 10 (4): 286–293. doi :10.1112/jlms/s1-10.40.286 .
Wright, E. M. (1940). "The asymptotic expansion of the generalized hypergeometric function". Proc. London Math. Soc . 46 (2): 389–408. doi :10.1112/plms/s2-46.1.389 .
Wright, E. M. (1952). "Erratum to "The asymptotic expansion of the generalized hypergeometric function" " . J. London Math. Soc . 27 : 254. doi :10.1112/plms/s2-54.3.254-s .
Srivastava, H.M.; Manocha, H.L. (1984). A treatise on generating functions . E. Horwood. ISBN 0-470-20010-3 .
Miller, A. R.; Moskowitz, I.S. (1995). "Reduction of a Class of Fox–Wright Psi Functions for Certain Rational Parameters" . Computers Math. Applic . 30 (11): 73–82. doi :10.1016/0898-1221(95)00165-u .
Sun, Jingchao; Kong, Maiying; Pal, Subhadip (22 June 2021). "The Modified-Half-Normal distribution: Properties and an efficient sampling scheme" . Communications in Statistics – Theory and Methods . 52 (5): 1591–1613. doi :10.1080/03610926.2021.1934700 . ISSN 0361-0926 . S2CID 237919587 .
External links