dist.Multivariate.Normal: Multivariate Normal Distribution

In LaplacesDemon: Complete Environment for Bayesian Inference

Description

These functions provide the density and random number generation for the multivariate normal distribution.

Usage

dmvn(x, mu, Sigma, log=FALSE) 
rmvn(n=1, mu, Sigma)

Arguments

x	This is data or parameters in the form of a vector of length k or a matrix with k columns.
n	This is the number of random draws.
mu	This is mean vector mu with length k or matrix with k columns.
Sigma	This is the k x k covariance matrix Sigma.
log	Logical. If log=TRUE, then the logarithm of the density is returned.

Details

• Application: Continuous Multivariate

• Density: p(theta) = (1/((2*pi)^(k/2)*|Sigma|^(1/2))) * exp(-(1/2)*(theta-mu)'*Sigma^(-1)*(theta-mu))

• Inventors: Robert Adrain (1808), Pierre-Simon Laplace (1812), and Francis Galton (1885)

• Notation 1: theta ~ MVN(mu, Sigma)

• Notation 2: theta ~ N[k](mu, Sigma)

• Notation 3: p(theta) = MVN(theta | mu, Sigma)

• Notation 4: p(theta) = N[k](theta | mu, Sigma)

• Parameter 1: location vector mu

• Parameter 2: positive-definite k x k covariance matrix Sigma

• Mean: E(theta) = mu

• Variance: var(theta) = Sigma

• Mode: mode(theta) = mu

The multivariate normal distribution, or multivariate Gaussian distribution, is a multidimensional extension of the one-dimensional or univariate normal (or Gaussian) distribution. A random vector is considered to be multivariate normally distributed if every linear combination of its components has a univariate normal distribution. This distribution has a mean parameter vector mu of length k and a k x k covariance matrix Sigma, which must be positive-definite.

The conjugate prior of the mean vector is another multivariate normal distribution. The conjugate prior of the covariance matrix is the inverse Wishart distribution (see dinvwishart).

When applicable, the alternative Cholesky parameterization should be preferred. For more information, see dmvnc.

For models where the dependent variable, Y, is specified to be distributed multivariate normal given the model, the Mardia test (see plot.demonoid.ppc, plot.laplace.ppc, or plot.pmc.ppc) may be used to test the residuals.

Value

dmvn gives the density and rmvn generates random deviates.

Author(s)

Statisticat, LLC. [e-mail]

See Also

dinvwishart, dmatrixnorm, dmvnc, dmvnp, dnorm, dnormp, dnormv, plot.demonoid.ppc, plot.laplace.ppc, and plot.pmc.ppc.

Examples

library(LaplacesDemon)
x <- dmvn(c(1,2,3), c(0,1,2), diag(3))
X <- rmvn(1000, c(0,1,2), diag(3))
joint.density.plot(X[,1], X[,2], color=TRUE)

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