Robust and scalable Bayes via a median of subset posterior measures

@article{Minsker2017RobustAS,
  title={Robust and scalable Bayes via a median of subset posterior measures},
  author={Stanislav Minsker and Sanvesh Srivastava and Lizhen Lin and David B. Dunson},
  journal={Journal of Machine Learning Research},
  year={2017},
  volume={18},
  pages={124:1-124:40}
}
We propose a novel approach to Bayesian analysis that is provably robust to outliers in the data and often has computational advantages over standard methods. Our technique is based on splitting the data into non-overlapping subgroups, evaluating the posterior distribution given each independent subgroup, and then combining the resulting measures. The main novelty of our approach is the proposed aggregation step, which is based on the evaluation of a median in the space of probability measures… CONTINUE READING
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