PMC:4307189 / 16805-17330 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4307189","sourcedb":"PMC","sourceid":"4307189","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4307189","text":"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].","tracks":[]}