Laplace Distribution

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The Laplace distribution, also called the double exponential distribution, is the distribution of differences between two independent variates with identical exponential distributions (Abramowitz and Stegun 1972, p. 930). It had probability density function and cumulative distribution functions given by

P(x)=1/(2b)e^(-|x-mu|/b)
(1)
D(x)=1/2[1+sgn(x-mu)(1-e^(-|x-mu|/b))].
(2)

It is implemented in the Wolfram Language as LaplaceDistribution[mu, beta].

The moments about the mean mu_n are related to the moments about 0 by

 mu_n=sum_(j=0)^n(n; j)(-1)^(n-j)mu_j^'mu^(n-j),
(3)

where (n; k) is a binomial coefficient, so

mu_n=sum_(j=0)^(n)sum_(k=0)^(|_j/2_|)(-1)^(n-j)(n; j)(j; 2k)b^(2k)mu^(n-2k)Gamma(2k+1)
(4)
={n!b^n for n even; 0 for n odd,
(5)

where |_x_| is the floor function and Gamma(2k+1) is the gamma function.

The moments can also be computed using the characteristic function,

 phi(t)=int_(-infty)^inftye^(itx)P(x)dx=1/(2b)int_(-infty)^inftye^(itx)e^(-|x-mu|/b)dx.
(6)

Using the Fourier transform of the exponential function

 F_x[e^(-2pik_0|x|)](k)=1/pi(k_0)/(k^2+k_0^2)
(7)

gives

 phi(t)=(e^(imut))/(1+b^2t^2)
(8)

(Abramowitz and Stegun 1972, p. 930). The moments are therefore

 mu_n=(-i)^nphi(0)=(-i)^n[(d^nphi)/(dt^n)]_(t=0).
(9)

The mean, variance, skewness, and kurtosis excess are

mu=mu
(10)
sigma^2=2b^2
(11)
gamma_1=0
(12)
gamma_2=3.
(13)

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