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\name{rwish}
\alias{rwish}
\title{ Sampling from Wishart distribution }
\description{
Generates random matrices, distributed according to the Wishart distribution with parameters \eqn{b} and \eqn{D}, \eqn{W(b, D)}.
}
\usage{ rwish( n = 1, p = 2, b = 3, D = diag( p ) ) }
\arguments{
\item{n}{ number of samples required. }
\item{p}{ number of variables (nodes). }
\item{b}{ degree of freedom for Wishart distribution, \eqn{W(b, D)}. }
\item{D}{ positive definite \eqn{(p \times p)} "scale" matrix for Wishart distribution, \eqn{W(b, D)}.
The default is an identity matrix. }
}
\details{
Sampling from Wishart distribution, \eqn{K \sim W(b, D)}, with density:
\deqn{Pr(K) \propto |K| ^ {(b - 2) / 2} \exp \left\{- \frac{1}{2} \mbox{trace}(K \times D)\right\},}
which \eqn{b > 2} is the degree of freedom and \eqn{D} is a symmetric positive definite matrix.
}
\value{
A numeric array, say \eqn{A}, of dimension \eqn{(p \times p \times n)}, where each \eqn{A[,,i]}{A[,,i]} is a positive
definite matrix, a realization of the Wishart distribution \eqn{W(b, D)}.
Note, for the case \eqn{n=1}, the output is a matrix.
}
\references{
Lenkoski, A. (2013). A direct sampler for G-Wishart variates, \emph{Stat}, 2:119-128, \doi{10.1002/sta4.23}
Mohammadi, R. and Wit, E. C. (2019). \pkg{BDgraph}: An \code{R} Package for Bayesian Structure Learning in Graphical Models, \emph{Journal of Statistical Software}, 89(3):1-30, \doi{10.18637/jss.v089.i03}
}
\author{ Reza Mohammadi \email{a.mohammadi@uva.nl} }
\seealso{ \code{\link{gnorm}}, \code{\link{rgwish}} }
\examples{
sample <- rwish( n = 3, p = 5, b = 3, D = diag( 5 ) )
round( sample, 2 )
}
\keyword{Wishart}
\keyword{distribution}
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