Risk and regret of hierarchical Bayesian learners
Common statistical practice has shown that the full power of Bayesian methods is not realized until hierarchical priors are used, as these allow for greater "robustness" and the ability to "share statistical strength." Yet it is an ongoing challenge to provide a learning-theoreti...
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| Format: | Article |
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Journal of Machine Learning Research/Microtome Publishing
2017
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| Online Access: | http://hdl.handle.net/1721.1/112754 https://orcid.org/0000-0002-9256-6727 https://orcid.org/0000-0002-1925-2035 |