A major limitation of the p1-model is the difficulty of calculating the normalizing constant, κ(θ), since it is a sum over the entire graph space. Estimating the maximum likelihood of this model becomes intractable as there are 2g(g−1) possible directed graphs (or 2g(g−1)2 undirected graphs), each having g nodes (genes). A technique called maximum pseudolikelihood estimation has been developed to address this problem [27]. This technique employs MCMC methods such as Gibbs or Metropolis-Hastings sampling algorithms [28].