The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models
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DOI: 10.1080/10618600.2022.2050250
Abstract: Gaussian graphical models can capture complex dependency structures amongst variables. For such models, Bayesian inference is attractive as it provides principled ways to incorporate prior information and to quantify uncertainty through the posterior distribution. However,… read more here.
Keywords: gaussian graphical; posterior computation; graphical models; proposal ... See more keywords