Rev Language Reference


dnMultivariateNormal - Multivariate Normal Distribution

The multivariate normal distribution has the probability density: f(x) = det(2 pi Sigma)^(-1/2) e^{-(1/2) (x-mu)' Sigma^-1 (x-mu)} where mu is a vector of mean values and Sigma is a covariance matrix. Note, this distribution may also be parameterized in terms of the precision matrix, Sigma^-1.

Usage

dnMultivariateNormal(Real[] mean, MatrixRealSymmetric covariance, MatrixRealSymmetric precision, RealPos scale)

Arguments

mean : Real[] (pass by const reference)
The vector of mean values.
covariance : MatrixRealSymmetric (pass by const reference)
The variance-covariance matrix.
Default : NULL
precision : MatrixRealSymmetric (pass by const reference)
The precision matrix.
Default : NULL
scale : RealPos (pass by const reference)
The scaling factor of the variance matrix.
Default : 1

Domain Type

Real[]

Example

dim = 4
df = 100
kappa <- 2
Sigma ~ dnWishart(df, kappa, dim)
for (i in 1:dim) { mu[i] ~ dnUnif(-1, 1) }
x ~ dnMultivariateNormal( mean=mu, covariance=Sigma )
mv[1] = mvCorrelationMatrixElementSwap(Sigma)
mv[2] = mvCorrelationMatrixRandomWalk(Sigma)
mv[3] = mvCorrelationMatrixSingleElementBeta(Sigma)
mv[4] = mvCorrelationMatrixSpecificElementBeta(Sigma)
mv[5] = mvCorrelationMatrixUpdate(Sigma)
mv[6] = mvVectorSlide(x)

Methods

See Also