a review on bayesian structure learning in gaussian graphical models

An accurate interpretation of complicated relations among a large number of variables is of significant importance in science. one appealing approach to this task is gaussian graphical models (ggms), which lately many improvements have been carried out on it. ggms describe the conditional independence of variables through the presence or absence of edges in the underlying 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.