Fast robustness quantification with variational Bayes
Machine Learning
2016-06-24 v1 Applications
Methodology
Abstract
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.
Cite
@article{arxiv.1606.07153,
title = {Fast robustness quantification with variational Bayes},
author = {Ryan Giordano and Tamara Broderick and Rachael Meager and Jonathan Huggins and Michael Jordan},
journal= {arXiv preprint arXiv:1606.07153},
year = {2016}
}
Comments
presented at 2016 ICML Workshop on #Data4Good: Machine Learning in Social Good Applications, New York, NY