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\name{lndMvn}
\alias{lndMvn}
\concept{multivariate normal distribution}
\concept{density}
\title{ Compute Log of Multivariate Normal Density }
\description{
\code{lndMvn} computes the log of a Multivariate Normal Density.
}
\usage{lndMvn(x, mu, rooti)}
\arguments{
\item{x }{ density ordinate }
\item{mu }{ mu vector }
\item{rooti}{ inv of upper triangular Cholesky root of \eqn{\Sigma} }
}
\details{
\eqn{z} \eqn{\sim}{~} \eqn{N(mu,\Sigma)}
}
\value{Log density value}
\section{Warning}{
This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type.
}
\author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.}
\references{For further discussion, see Chapter 2, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. }
\seealso{ \code{\link{lndMvst}} }
\examples{
Sigma = matrix(c(1, 0.5, 0.5, 1), ncol=2)
lndMvn(x=c(rep(0,2)), mu=c(rep(0,2)), rooti=backsolve(chol(Sigma),diag(2)))
}
\keyword{distribution}
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