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Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes
Ryan Giordano · Tamara Broderick · Michael Jordan

Tue Dec 08 12:30 PM -- 01:00 PM (PST) @ Room 210 A

Mean field variational Bayes (MFVB) is a popular posterior approximation method due to its fast runtime on large-scale data sets. However, a well known failing of MFVB is that it underestimates the uncertainty of model variables (sometimes severely) and provides no information about model variable covariance. We generalize linear response methods from statistical physics to deliver accurate uncertainty estimates for model variables---both for individual variables and coherently across variables. We call our method linear response variational Bayes (LRVB). When the MFVB posterior approximation is in the exponential family, LRVB has a simple, analytic form, even for non-conjugate models. Indeed, we make no assumptions about the form of the true posterior. We demonstrate the accuracy and scalability of our method on a range of models for both simulated and real data.

Author Information

Ryan Giordano (UC Berkeley)
Tamara Broderick (MIT)
Michael Jordan (UC Berkeley)

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