Introduction
The Gamma function, as initiated for factorial problems, is ingeniously set forth by the infinite integral function (IF) in single exponent (SE) of
for
(
in the case that
is a complex number) [
1,
2,
3,
4,
5]. This integral function is the most applied form of the factorial function though other extensions exist and extends to the entire complex plane via analytic continuation except non-positive integers which just result in simple poles.
In a variety of fields such as probability and statistics, quantum physics, string theory, artificial intelligence, and machine learning, this beautiful Gamma function finds a broad range of applications exemplar in the appearance of gamma distribution, beta function, Dirichlet’s distribution, Stirling’s asymptotic expansion, the zeta function and Riemann’s hypothesis.
In contrast, the WJ distribution is a double exponential (or bi-exponent (BE)) probability distribution function [
6], described by the following formula
or in equivalence
with
(
is the digamma function), defined over the domain of (−
, +
). According to Equations (2) or (3), the WJ distribution function has three different free parameters
,
and
: The parameter
shifts the location of the curve but does not exert effect on the shape of the distribution. The parameters
and
taking positive values jointly govern the shape of the curve and make a contribution of
to the horizontal setting of the curve.
The WJ distribution function, unifying a series of classical distributions, affords effectively a universal mechanism to tackle extreme events and critical phenomena, ranging from extraordinary occurrences to critical properties, enormously diverse in types and disparate in properties [
6,
7,
8,
9]. Still, the WJ distribution function has been corroborated to be an appropriate procedure in analyzing the Kohlrausch-Williams-Watts (KWW) relaxation [
10] and well representing the Gaussian function with a specific set of the parameters (
,
,
) [
11], along with potential application in information theory [
12].
Results
In this work, we shall establish a close linkage between the Gamma function and the WJ distribution. We define a bi-exponent integral function (BEIF),
, in the vein of the WJ distribution
Alternatively, the same definition applies to
,
which will be incurred in the process of deduction later.
By rearranging the terms in Equation (4), we obtain
Conducting the partial integration, Equation (6) turns out to be
which leads to the expression as follows
Considering the fact that the argument
has the same outcome as that of
for the infinite integral, thus Equation (8) adopts the formulation
The part in the great bracket is analogous to Equation (2) with the variable set (
) being changed to (
), that is, the corresponding integral function assumes the form
Evidently, we get a recurrence relation for the bi-exponent integral function
It is perceived that Equation (11) is the same form as the recurrence relation of the Gamma function in disguise
In point of fact, Equation (4) is reducible to the following single-exponent infinite integral (SEIF)
which yields in essence the same expression as Equation (1) for the Gamma function with
, after we carry out the exchange of the variables
Moreover, it is recognized that
is scalable to
, viz.,
Nonetheless, the association of the Gamma function with the WJ distribution may be instructively scrutinized from the embedding of the former in the formula of the latter (cf. Equations (2) & (3)). Since the WJ distribution is a probability distribution function and has intrinsically the unitary property of the total integration
we may directly acquire the Gamma function given in the argument of
by inserting the explicit formula of
in Equation (2) and rearranging
which is identical to Equation (4). Consequently, we have demonstrated that the Gamma function may be naturally inferred from the WJ distribution
, or in other words, the bi-exponent integral function is equivalent to the Gamma function. Conclusively, it is appreciated that the BEIF of Equation (4) is correspondent to the Gamma function of Equation (1), which is particularly transparent in its reduced form of Equation (13).
Figure 1 presents the amplitude of the Gamma function
or equivalently
in complex plane in 3D created with Matlab & Origin.
Concerning the probability distribution function for the Gamma function, we come to two representations, one of which is designated by the original WJ distribution
of Equation (2) (or equivalent
of Equation (3)) and the other is, based on Equation (13), the classic form of the Gamma distribution specified by
Their first and second statistical quantities, the expectation
and the square deviation
, respectively, read
and
for the original WJ distribution, as well as
and
for the classical expression of the Gamma distribution derivable from the original WJ distribution.
It is immediately noticed that the two kinds of the Gamma distribution, essentially resulting from the WJ distribution, have quite distinctive characteristics, as partially reflected in their statistical quantities as above-elucidated. Relating to the expectation of the original WJ distribution (cf. Equation (19)),
Figure 2 plots the amplitude of the digamma function
or equivalently
in complex plane in 3D created with Matlab & Origin for direct visualization.
In addition, we can conduct Fourier or Laplace transformation on these two different representations, separately. As the WJ distribution is defined over the whole domain (
,
), a Fourier transform is pertinent
By replacing the expression of Equation (2), we have the explicit formulation of
for the transform. Correspondingly, a Laplace transform can be performed on the classical form of the Gamma distribution in the form of Equation (18)
or explicitly