Laplace Distribution
Definition1
For and , a continuous probability distribution with the following probability density function is called the Laplace distribution.
Explanation
Relationship with the Normal Distribution
Although it looks similar to the normal distribution, it has an absolute value instead of a square, giving it a sharper shape. Near , the Laplace distribution decreases much more rapidly, while farther from , the normal distribution decreases more rapidly.
Relationship with the Exponential Distribution
Particularly when and in the Laplace distribution, it coincides with the exponential distribution when . It can be seen as an extension of the exponential distribution defined in to . If , then .
Basic Properties
Moment Generating Function
The moment generating function for the Laplace distribution is given by:
Mean and Variance
If , then
Maximum Likelihood Estimator
Suppose that a random sample is given.
The maximum likelihood estimator for is as follows:
Visualization
The following is a Julia code snippet illustrating the probability density function of the Laplace distribution as a GIF.
@time using LaTeXStrings
@time using Distributions
@time using Plots
cd(@__DIR__)
x = -10:0.1:10
B = collect(0.1:0.1:5.0); append!(B, reverse(B))
animation = @animate for b ∈ B
plot(x, pdf.(Laplace(0, b), x),
color = :black,
label = "b = $(round(b, digits = 2))", size = (400,300))
xlims!(-10,10); ylims!(0,1); title!(L"\mathrm{pdf\,of\,} \operatorname{Laplace}(0, b)")
end
gif(animation, "pdf.gif", fps = 15)
plot(x, pdf.(Laplace(0, 1), x), lw=2, label="Laplace(0, 1)", color=:royalblue, dpi=200)
plot!(x, pdf.(Normal(0, 1), x), lw=2, label="Normal(0, 1)", color=:tomato)
Hogg et al. (2018). Introduction to Mathematical Statistcs(8th Edition): p77 ↩︎