A Variational Bayes Approach to Clustered Latent Preference Models for Directed Network Data
Variational Bayes (VB) refers to a framework used to make fast deterministic approximations to the posterior density for Bayesian statistical inference. Traditionally, it has competed with Markov Chain Monte Carlo (MCMC) methods, a stochastic method which is asymptotically correct but computationally expensive. We derive the VB approximation to the Directed Clustered Latent Preference Network Model, which is inspired by ideas from Hoff et al. (2002); Handcock...[Show more]
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