\name{I.g}
\alias{I.g}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Computing normalizing constant of G-Wishart distribution
}
\description{
Monte Carlo method for approximating the normalizing constant of G-Wishart
distribution. The function uses the Monte Carlo method of Atay-Kayis and Massam (2005).
}
\usage{
I.g(A, b, D, MCiter = 500)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
  \item{A}{
Upper truculer matrix in which a_ij=1 if there is a link between notes i and j, otherwise a_ij=0.
}
  \item{b}{
The degree of freedom for G-Wishart distribution, W_G(b,D).
}
  \item{D}{
Positive definite matrix for G-Wishart distribution, W_G(b,D).
}
  \item{MCiter}{
The number of iterations for the Monte Carlo approximation.
}
}
\value{
The normalizing constant of G-Wishart distribution.
}
\references{
Mohammadi, A. and E. Wit (2012). Efficient birth-death MCMC inference for 
Gaussian graphical models, Journal of the Royal Statistical Society: Series B,
submitted.

Atay-Kayis, A. and H. Massam (2005). A monte carlo method for computing the 
marginal likelihood in nondecomposable gaussian graphical models. Biometrika 
92(2), 317-335.
}
\author{
Abdolreza Mohammadi and Ernst Wit
}
\examples{
p=3
A=matrix(c(0,1,1,0,0,1,0,0,0),p,p,byrow=TRUE) # full graph with three nodes and three links
I.g(A, b=3, D=diag(p), MCiter=50)
}