BDgraph (version 1.0)

sample.G.Wishart: Sampling from G-Wishart distribution

Description

Tools for sampling from G-Wishart distribution according to Choleski decomposition of a Wishart variate with the identity as its scale parameter.

Usage

sample.G.Wishart(A, b, D)

Arguments

A

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

b

value for prior distribution of precision matrix

D

positive definite matrix for prior distribution of precision matrix

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=5
b=3; D=diag(p)
A=matrix(0,p,p)
for (i in 1:(p-1)){
   for (j in (i+1):p){ 
      if (j==i+1) A[i,j]=1
      } 
   }
A[1,p]=1
round(sample.G.Wishart(A,b,D),2)