dist.Multivariate.Cauchy.Precision: Multivariate Cauchy Distribution: Precision Parameterization

In LaplacesDemonR/LaplacesDemon: Complete Environment for Bayesian Inference

Multivariate Cauchy Distribution: Precision Parameterization

Description

These functions provide the density and random number generation for the multivariate Cauchy distribution. These functions use the precision parameterization.

Usage

dmvcp(x, mu, Omega, log=FALSE)
rmvcp(n=1, mu, Omega)

Arguments

x	This is either a vector of length k or a matrix with a number of columns, k, equal to the number of columns in precision matrix \Omega.
n	This is the number of random draws.
mu	This is a numeric vector representing the location parameter, \mu (the mean vector), of the multivariate distribution. It must be of length k, as defined above.
Omega	This is a k \times k positive-definite precision matrix \Omega.
log	Logical. If log=TRUE, then the logarithm of the density is returned.

Details

• Application: Continuous Multivariate

• Density:

  p(\theta) = \frac{\Gamma((1+k)/2)}{\Gamma(1/2)1^{k/2}\pi^{k/2}} |\Omega|^{1/2} (1 + (\theta-\mu)^T \Omega (\theta-\mu))^{-(1+k)/2}

• Inventor: Unknown (to me, anyway)

• Notation 1: \theta \sim \mathcal{MC}_k(\mu, \Omega^{-1})

• Notation 2: p(\theta) = \mathcal{MC}_k(\theta | \mu, \Omega^{-1})

• Parameter 1: location vector \mu

• Parameter 2: positive-definite k \times k precision matrix \Omega

• Mean: E(\theta) = \mu

• Variance: var(\theta) = undefined

• Mode: mode(\theta) = \mu

The multivariate Cauchy distribution is a multidimensional extension of the one-dimensional or univariate Cauchy distribution. A random vector is considered to be multivariate Cauchy-distributed if every linear combination of its components has a univariate Cauchy distribution. The multivariate Cauchy distribution is equivalent to a multivariate t distribution with 1 degree of freedom.

The Cauchy distribution is known as a pathological distribution because its mean and variance are undefined, and it does not satisfy the central limit theorem.

It is usually parameterized with mean and a covariance matrix, or in Bayesian inference, with mean and a precision matrix, where the precision matrix is the matrix inverse of the covariance matrix. These functions provide the precision parameterization for convenience and familiarity. It is easier to calculate a multivariate Cauchy density with the precision parameterization, because a matrix inversion can be avoided.

This distribution has a mean parameter vector \mu of length k, and a k \times k precision matrix \Omega, which must be positive-definite.

Value

dmvcp gives the density and rmvcp generates random deviates.

Author(s)

Statisticat, LLC. [e-mail]

See Also

dcauchy, dmvc, dmvt, dmvtp, and dwishart.

Examples

library(LaplacesDemon)
x <- seq(-2,4,length=21)
y <- 2*x+10
z <- x+cos(y) 
mu <- c(1,12,2)
Omega <- matrix(c(1,2,0,2,5,0.5,0,0.5,3), 3, 3)
f <- dmvcp(cbind(x,y,z), mu, Omega)

X <- rmvcp(1000, rep(0,2), diag(2))
X <- X[rowSums((X >= quantile(X, probs=0.025)) &
     (X <= quantile(X, probs=0.975)))==2,]
joint.density.plot(X[,1], X[,2], color=TRUE)

LaplacesDemonR/LaplacesDemon documentation built on April 1, 2024, 7:22 a.m.