https://github.com/cran/BDgraph
Tip revision: 72b95efce3f7c808386f6e86b3789d004a213bf5 authored by Reza Mohammadi on 25 December 2022, 06:20:14 UTC
version 2.72
version 2.72
Tip revision: 72b95ef
gnorm.Rd
\name{gnorm}
\alias{gnorm}
\title{ Normalizing constant for G-Wishart }
\description{
Calculates log of the normalizing constant of G-Wishart distribution based on the Monte Carlo method, developed by Atay-Kayis and Massam (2005).
}
\usage{
gnorm( adj, b = 3, D = diag( ncol( adj ) ), iter = 100 )
}
\arguments{
\item{adj}{
adjacency matrix corresponding to the graph structure. It is an upper triangular matrix in which
\eqn{a_{ij}=1}{aij = 1} if there is a link between notes \eqn{i}{i} and \eqn{j}{j},
otherwise \eqn{a_{ij}=0}{aij = 0}.
}
\item{b}{ degree of freedom for G-Wishart distribution, \eqn{W_G(b, D)}. }
\item{D}{
positive definite \eqn{(p \times p)} "scale" matrix for G-Wishart distribution,
\eqn{W_G(b,D)}. The default is an identity matrix.
}
\item{iter}{number of iteration for the Monte Carlo approximation.}
}
\details{
Log of the normalizing constant approximation using Monte Carlo method for a G-Wishart distribution, \eqn{K \sim W_G(b, D)}, with density:
\deqn{Pr(K) = \frac{1}{I(b, D)} |K| ^ {(b - 2) / 2} \exp \left\{- \frac{1}{2} \mbox{trace}(K \times D)\right\}.}
}
\value{ Log of the normalizing constant of G-Wishart distribution. }
\references{
Atay-Kayis, A. and Massam, H. (2005). A monte carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models, \emph{Biometrika}, 92(2):317-335, \doi{10.1093/biomet/92.2.317}
Mohammadi, R., Massam, H. and Letac, G. (2021). Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models, \emph{Journal of the American Statistical Association}, \doi{10.1080/01621459.2021.1996377}
Uhler, C., et al (2018) Exact formulas for the normalizing constants of Wishart distributions for graphical models, \emph{The Annals of Statistics} 46(1):90-118, \doi{10.1214/17-AOS1543}
}
\author{ Reza Mohammadi \email{a.mohammadi@uva.nl} }
\seealso{ \code{\link{rgwish}}, \code{\link{rwish}} }
\examples{
\dontrun{
# adj: adjacency matrix of graph with 3 nodes and 2 links
adj <- matrix( c( 0, 0, 1,
0, 0, 1,
0, 0, 0 ), 3, 3, byrow = TRUE )
gnorm( adj, b = 3, D = diag( 3 ) )
}
}
\keyword{Wishart}
\keyword{iteration}