سعید طهماسبی

An accurate understanding of complicated relations among numerous variables is of significant importance in science. One attractive procedure to this task is Gaussian graphical models (GGMs), which lately many improvements have been carried out on it. GGMs describe the conditional independence among variables by means of the presence or absence of edges in the related graph. In this paper, we recap a Bayesian method for structure learning of GGMs based on the Birth-Death MCMC (BDMCMC) algorithm. We show the application of this method on a simulated dataset.