1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
|
\name{lndMvst}
\alias{lndMvst}
\concept{multivariate t distribution}
\concept{student-t distribution}
\concept{density}
\title{Compute Log of Multivariate Student-t Density}
\description{
\code{lndMvst} computes the log of a Multivariate Student-t Density.
}
\usage{lndMvst(x, nu, mu, rooti, NORMC)}
\arguments{
\item{x }{ density ordinate }
\item{nu }{ d.f. parameter }
\item{mu }{ mu vector }
\item{rooti}{ inv of Cholesky root of \eqn{\Sigma} }
\item{NORMC}{ include normalizing constant (def: \code{FALSE}) }
}
\details{
\eqn{z} \eqn{\sim}{~} \eqn{MVst(mu, nu, \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{lndMvn}} }
\examples{
Sigma = matrix(c(1, 0.5, 0.5, 1), ncol=2)
lndMvst(x=c(rep(0,2)), nu=4,mu=c(rep(0,2)), rooti=backsolve(chol(Sigma),diag(2)))
}
\keyword{distribution}
|
