Laplace distribution

From Wikipedia, the free encyclopedia

(Redirected from Laplacian distribution)

Laplace
Probability density function
Cumulative distribution function
Parameters	$\mu\,$ location (real) $b > 0\,$ scale (real)
Support	$x \in (-\infty; +\infty)\,$
Probability density function (pdf)	$\frac{1}{2\,b} \exp \left(-\frac{\|x-\mu\|}b \right) \,$
Cumulative distribution function (cdf)	see text
Mean	$\mu\,$
Median	$\mu\,$
Mode	$\mu\,$
Variance	$2\,b^2$
Skewness	$0\,$
Excess kurtosis	$3\,$
Entropy	$\log(2\,e\,b)$
Moment-generating function (mgf)	$\frac{\exp(\mu\,t)}{1-b^2\,t^2}\,\!$ for $\|t\|<1/b\,$
Characteristic function	$\frac{\exp(\mu\,i\,t)}{1+b^2\,t^2}\,\!$

In probability theory and statistics, the Laplace distribution is a continuous probability distribution named after Pierre-Simon Laplace. It is also known as the double exponential distribution, because it can be thought of as two exponential distributions (with an additional location parameter) spliced together back-to-back. The difference between two independent identically distributed exponential random variables is governed by a Laplace distribution, as is a Brownian motion evaluated at an exponentially distributed random time.

1 Characterization
- 1.1 Probability density function
- 1.2 Cumulative distribution function
2 Generating Laplace variates
3 Parameter estimation
4 Moments
5 Related distributions
6 See also
7 References

A random variable has a Laplace(μ, b) distribution if its probability density function is

$f(x|\mu,b) = \frac{1}{2b} \exp \left( -\frac{|x-\mu|}{b} \right) \,\!$

$= \frac{1}{2b} \left\{\begin{matrix} \exp \left( -\frac{\mu-x}{b} \right) & \mbox{if }x < \mu \\[8pt] \exp \left( -\frac{x-\mu}{b} \right) & \mbox{if }x \geq \mu \end{matrix}\right.$

Here, μ is a location parameter and b > 0 is a scale parameter. If μ = 0 and b = 1, the positive half-line is exactly an exponential distribution scaled by 1/2.

The pdf of the Laplace distribution is also reminiscent of the normal distribution; however, whereas the normal distribution is expressed in terms of the squared difference from the mean μ, the Laplace density is expressed in terms of the absolute difference from the mean. Consequently the Laplace distribution has fatter tails than the normal distribution.

The Laplace distribution is easy to integrate (if one distinguishes two symmetric cases) due to the use of the absolute value function. Its cumulative distribution function is as follows:

$F(x)\,$	$= \int_{-\infty}^x \!\!f(u)\,\mathrm{d}u$
	$= \left\{\begin{matrix} &\frac12 \exp \left( -\frac{\mu-x}{b} \right) & \mbox{if }x < \mu \\[8pt] 1-\!\!\!\!&\frac12 \exp \left( -\frac{x-\mu}{b} \right) & \mbox{if }x \geq \mu \end{matrix}\right.$
	$=0.5\,[1 + \sgn(x-\mu)\,(1-\exp(-\|x-\mu\|/b))].$

The inverse cumulative distribution function is given by

$F^{-1}(p) = \mu - b\,\sgn(p-0.5)\,\ln(1 - 2|p-0.5|).$

Given a random variate U drawn from the uniform distribution in the interval (-1/2, 1/2], the variate

$X=\mu - b\,\sgn(U)\,\ln(1 - 2|U|)$

has a Laplace distribution with parameters μ and b. This follows from the inverse cumulative distribution function given above.

A Laplace(0, b) variate can also be generated as the difference of two i.i.d. Exponential(1/b) variates. Equivalently, a Laplace(0, 1) variate can be generated as the logarithm of the ratio of two iid uniform variates.

Given N independent and identically distributed samples x₁, x₂, ..., x_N, estimator of $μ$ , $\hat{\mu}$ is the sample median^[1], and the estimator of b is

$\hat{b} = \frac{1}{N} \sum_{i = 1}^{N} |x_i - \hat{\mu}|,$

using the maximum likelihood estimator.

$\mu_r' = \bigg({\frac{1}{2}}\bigg) \sum_{k=0}^r \bigg[{\frac{r!}{k! (r-k)!}} b^k \mu^{(r-k)} k! \{1 + (-1)^k\}\bigg]$

If $X \sim \mathrm{Laplace}(0,b)\,$ then $|X| \sim \mathrm{Exponential}(b^{-1})\,$ is an exponential distribution.
If $X \sim \mathrm{Exponential}(\lambda)\,$ and $Y \sim \mathrm{Bernoulli}(0.5)\,$ independent of $X\,$ , then $X(2Y-1) \sim \mathrm{Laplace} (0,\lambda^{-1}) \,$ .
If $X_1 \sim \mathrm{Exponential}(\lambda_1)\,$ and $X_2 \sim \mathrm{Exponential}(\lambda_2)\,$ independent of $X_1\,$ , then $\lambda_1 X_1-\lambda_2 X_2 \sim \mathrm{Laplace}\left(0,1\right)\,$ ．

Log-Laplace distribution

^ [1] Robert M. Norton,Double Exponential Distribution: Using Calculus to Find a Maximum Likelihood Estimator The American Statistician, Vol. 38, No. 2. (May, 1984), pp. 135-136.

	Probability distributions [ view • talk • edit ]
	Univariate	Multivariate
Discrete:	Benford • Bernoulli • binomial • Boltzmann • categorical • compound Poisson • discrete phase-type • degenerate • Gauss-Kuzmin • geometric • hypergeometric • logarithmic • negative binomial • parabolic fractal • Poisson • Rademacher • Skellam • uniform • Yule-Simon • zeta • Zipf • Zipf-Mandelbrot	Ewens • multinomial • multivariate Polya
Continuous:	Beta • Beta prime • Cauchy • chi-square • Dirac delta function • Coxian • Erlang • exponential • exponential power • F • fading • Fermi-Dirac • Fisher's z • Fisher-Tippett • Gamma • generalized extreme value • generalized hyperbolic • generalized inverse Gaussian • Half-logistic • Hotelling's T-square • hyperbolic secant • hyper-exponential • hypoexponential • inverse chi-square (scaled inverse chi-square) • inverse Gaussian • inverse gamma (scaled inverse gamma) • Kumaraswamy • Landau • Laplace • Lévy • Lévy skew alpha-stable • logistic • log-normal • Maxwell-Boltzmann • Maxwell speed • Nakagami • normal (Gaussian) • normal-gamma • normal inverse Gaussian • Pareto • Pearson • phase-type • polar • raised cosine • Rayleigh • relativistic Breit-Wigner • Rice • shifted Gompertz • Student's t • triangular • truncated normal • type-1 Gumbel • type-2 Gumbel • uniform • Variance-Gamma • Voigt • von Mises • Weibull • Wigner semicircle • Wilks' lambda	Dirichlet • Generalized Dirichlet • inverse-Wishart • Kent • matrix normal • multivariate normal • multivariate Student • von Mises-Fisher • Wigner quasi • Wishart
Miscellaneous:	bimodal • Cantor • conditional • equilibrium • exponential family • Infinite divisibility (probability) • location-scale family • marginal • maximum entropy • posterior • prior • quasi • sampling • singular • unimodal

Retrieved from "http://en.wikipedia.org/wiki/Laplace_distribution"

Category: Continuous distributions

Searching reference...

Reference

Sources

Laplace distribution

From Wikipedia, the free encyclopedia

Contents

Top Matching Results

Sponsored Links
This section contains paid listings which have been purchased by companies that want to have their sites appear for specific search terms and related content. These listings are administered, sorted and maintained by a third party and are not endorsed by Search.com.

Search Results

Searching reference...

Reference

Sources

Laplace distribution

From Wikipedia, the free encyclopedia

Contents

Top Matching Results

Sponsored Links This section contains paid listings which have been purchased by companies that want to have their sites appear for specific search terms and related content. These listings are administered, sorted and maintained by a third party and are not endorsed by Search.com.

Search Results

Sponsored Links
This section contains paid listings which have been purchased by companies that want to have their sites appear for specific search terms and related content. These listings are administered, sorted and maintained by a third party and are not endorsed by Search.com.