Scalable Model Selection for Belief Networks

NeurIPS 2017 Zhao SongYusuke MuraokaRyohei FujimakiLawrence Carin

We propose a scalable algorithm for model selection in sigmoid belief networks (SBNs), based on the factorized asymptotic Bayesian (FAB) framework. We derive the corresponding generalized factorized information criterion (gFIC) for the SBN, which is proven to be statistically consistent with the marginal log-likelihood... (read more)

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