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About this paper
Nanwei Wang, Laurent Briollais, Helene Massam
The authors introduce SBDMCMC to learn mixed-data networks by searching each variable’s neighborhood rather than complete graphs. It models Gaussian, binary, and count variables with conditional models and uses BIC or extended BIC approximations in birth–death sampling. In simulations, it achieves higher F1 scores than BDMCMC and MGM on mixed scale-free and random networks, but not across all settings.
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