dist.Laplace.Precision: Laplace Distribution: Precision Parameterization

In LaplacesDemon: Complete Environment for Bayesian Inference

Description

These functions provide the density, distribution function, quantile function, and random generation for the univariate, symmetric, Laplace distribution with location parameter mu and precision parameter tau, which is the inverse of the usual scale parameter, lambda.

Usage

dlaplacep(x, mu=0, tau=1, log=FALSE)
plaplacep(q, mu=0, tau=1)
qlaplacep(p, mu=0, tau=1)
rlaplacep(n, mu=0, tau=1)

Arguments

x, q	These are each a vector of quantiles.
p	This is a vector of probabilities.
n	This is the number of observations, which must be a positive integer that has length 1.
mu	This is the location parameter mu.
tau	This is the precision parameter tau, which must be positive.
log	Logical. If log=TRUE, then the logarithm of the density is returned.

Details

• Application: Continuous Univariate

• Density: p(theta) = (tau/2) * exp(-tau*abs(theta-mu))

• Inventor: Pierre-Simon Laplace (1774)

• Notation 1: theta ~ Laplace(mu, tau^(-1))

• Notation 2: theta ~ L(mu, tau^(-1))

• Notation 3: p(theta) = Laplace(mu, tau^(-1))

• Notation 4: p(theta) = L(theta | mu, tau(-1))

• Parameter 1: location parameter mu

• Parameter 2: precision parameter tau > 0

• Mean: E(theta) = mu

• Variance: var(theta) = 2*tau^{-2}

• Mode: mode(theta) = mu

The Laplace distribution is also called the double exponential distribution, because it looks like two exponential distributions back to back with respect to location mu. It is also called the “First Law of Laplace”, just as the normal distribution is referred to as the “Second Law of Laplace”. The Laplace distribution is symmetric with respect to mu, though there are asymmetric versions of the Laplace distribution. The PDF of the Laplace distribution is reminiscent of the normal distribution; however, whereas the normal distribution is expressed in terms of the squared difference from the mean mu, the Laplace density is expressed in terms of the absolute difference from the mean, mu. Consequently, the Laplace distribution has fatter tails than the normal distribution. It has been argued that the Laplace distribution fits most things in nature better than the normal distribution. Elsewhere, there are a large number of extensions to the Laplace distribution, including asymmetric versions and multivariate versions, among many more. These functions provide the precision parameterization for convenience and familiarity in Bayesian inference.

Value

dlaplacep gives the density, plaplacep gives the distribution function, qlaplacep gives the quantile function, and rlaplacep generates random deviates.

Author(s)

Statisticat, LLC. [e-mail]

See Also

dalaplace, dexp, dlaplace, dmvl, dnorm, dnormp, and dnormv.

Examples

library(LaplacesDemon)
x <- dlaplacep(1,0,1)
x <- plaplacep(1,0,1)
x <- qlaplacep(0.5,0,1)
x <- rlaplacep(100,0,1)

#Plot Probability Functions
x <- seq(from=-5, to=5, by=0.1)
plot(x, dlaplacep(x,0,0.5), ylim=c(0,1), type="l", main="Probability Function",
     ylab="density", col="red")
lines(x, dlaplacep(x,0,1), type="l", col="green")
lines(x, dlaplacep(x,0,2), type="l", col="blue")
legend(2, 0.9, expression(paste(mu==0, ", ", tau==0.5),
     paste(mu==0, ", ", tau==1), paste(mu==0, ", ", tau==2)),
     lty=c(1,1,1), col=c("red","green","blue"))

Example output

LaplacesDemon documentation built on July 9, 2021, 5:07 p.m.