Fast robustness quantification with variational Bayes

Giordano, Ryan, Broderick, Tamara, Meager, Rachael, Huggins, Jonathan, Jordan, Michael

arXiv.org Machine Learning 

Bayesian hierarchical models are increasing popular in economics. When using hierarchical models, it is useful not only to calculate posterior expectations, but also to measure the robustness of these expectations to reasonable alternative prior choices. We use variational Bayes and linear response methods to provide fast, accurate posterior means and robustness measures with an application to measuring the effectiveness of microcredit in the developing world.

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