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...
| Main Authors: | , |
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| Other Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
Journal of Machine Learning Research/Microtome Publishing,
2017-12-14T15:44:11Z.
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| Subjects: | |
| Online Access: | Get fulltext |