Journal article
Robust and Scalable Bayes via a Median of Subset Posterior Measures
Journal of machine learning research, Vol.18, pp.1-40
01/01/2017
Abstract
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 equipped with a suitable collection of distances that can be quickly and efficiently evaluated in practice. We present both theoretical and numerical evidence illustrating the improvements achieved by our method.
Details
- Title: Subtitle
- Robust and Scalable Bayes via a Median of Subset Posterior Measures
- Creators
- Stanislav Minsker - Univ Southern Calif, Dept Mat, Los Angeles, CA 90089 USASanvesh Srivastava - Univ Iowa, Dept Stat & Actuarial Sci, Iowa City, IA 52242 USALizhen Lin - Univ Notre Dame, Dept Appl & Computat Math & Stat, Notre Dame, IN 46556 USADavid B Dunson - Duke University
- Resource Type
- Journal article
- Publication Details
- Journal of machine learning research, Vol.18, pp.1-40
- Publisher
- MICROTOME PUBL
- ISSN
- 1532-4435
- eISSN
- 1533-7928
- Number of pages
- 40
- Grant note
- R01-ES-017436 / National Institute of Environmental Health Sciences (NIEHS) of the National Institutes of Health (NIH) DMS-1712956; FODAVA CCF-0808847; DMS-0847388; ATD-1222567; DMS-1654579; IIS-1663870 / NSF
- Language
- English
- Date published
- 01/01/2017
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257613302771
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