BDgraph (version 1.0)

I_G: Computing normalizing constant of G-Wishart distribution

Description

Monte Carlo method for approximating the normalizing constant of G-Wishart distribution.

Usage

I_G(A, b, D, MC.iter = 300)

Arguments

A

Upper truculer matrix which shows the starting graphs in which a_ij=1 if there is a link between notes i and j, otherwise a_ij=0

b

Value for prior distribution of precision matrix

D

Positive definite matrix for prior distribution of precision matrix

MC.iter

Number of iterations for Monte Carlo approximation

Value

• 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.

Examples

p=3
A=matrix(c(0,1,1,0,0,1,0,0,0),p,p,byrow=TRUE) # full graph
I_G(A, b=3, D=diag(p), MC.iter=50)