BDgraph (version 2.3)

prob.allg: Posterior probability of all possible graphs

Description

According to the output of the BDMCMC algorithm, this function gives us the posterior probability of all possible graphical models. Aslo, it give us all graphs that the BDMCMC algorithm visits them.

Usage

prob.allg(output)

Arguments

output

a list which is the result of BDMCMC algorithm from the 'bdmcmc', 'bdmcmc.low', or 'bdmcmc.high' functions.

Value

• list.A
  a list which includes all the grpahs that the BDMCMC algorithm visits them.
• prob.A
  a vector which includes posterior probabilities of all graphs in 'list.A'.

References

Mohammadi, A. and E. C. Wit (2012). Gaussian graphical model determination based on birth-death MCMC inference, arXiv:1210.5371v4. http://arxiv.org/abs/1210.5371v4

See Also

bdmcmc

Examples

p <- 8 # number of nodes 
  # "truK" is the precision matrix of true graph
  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
  truK
  
  # generate the data (200 observations) from multivariate normal 
  # distribution with mean zero and percision matrix "truK"
  data <- mvrnorm(200, c(rep(0,p)), solve(truK))  
  
  # First, we run the 'bdmcmc' function for small number of iterations
  output <- bdmcmc(data, iter = 40, burn = 30, meanzero = T)
  prob.allg(output)