Covariance Matrices and Influence Scores for Mean Field Variational Bayes — mentioned by proditus

Sat, 19 Sep 2015 23:31:11 GMT

that's a really good question. some aspects of VI vs MCMC are areas of active research. so it's tough to respond succinctly, but i'll try. the key disadvantages of VI (particularly ADVI) are: 1. mean-field variational inference cannot model posterior correlations. so if you expect your model + dataset to give a "skewed" posterior, then mean-field variational inference will have a difficult time describing such a posterior. (it will under estimate marginal variances.) 2. full-rank variational inference can model posterior correlations. but it can become too expensive for big models. there is a…

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Covariance Matrices and Influence Scores for Mean Field Variational Bayes · HackerPapers discussions

Covariance Matrices and Influence Scores for Mean Field Variational Bayes — mentioned by proditus