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Abstract
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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.
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