BDgraph (version 1.0)

prob.allgraphs: Posterior probability of all possible grpahs

Description

According to the output of BDMCMC algorithm this function gives us the posterior probability of all possible graphical models.

Usage

prob.allgraphs(output)

Arguments

output

A list which is the result of BDMCMC algorithm

Value

• list.A
  A list which include all the grpahs that BDMCMC algorithm visit them
• prob.A
  A vector which include posterior probabilities of graphs in list.A

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.

See Also

BDMCMC

Examples

n=200; p=8
truK=diag(p)
for (i in 1:(p-1)) truK[i,i+1]=truK[i+1,i]=0.5
truK[1,p]=truK[p,1]=0.4
data=mvrnorm(n,c(rep(0,p)),solve(truK))
# we run our BDMCMC for small number of iterations, only for checking the result
output=BDMCMC(data,iter=40,burn=10)
prob.allgraphs(output)