Speaker
Ethan Neil
(University of Colorado, Boulder)
Description
Bayesian model averaging is a statistical method that allows for simple and methodical treatment of systematic errors due to model variation. I will summarize some recent results, including other model weights which can give more robust performance than the Akaike information criterion, as well as clarifying its use for data subset selection.
| Topical area | Algorithms and Artificial Intelligence |
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Primary author
Ethan Neil
(University of Colorado, Boulder)