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Creating structured and flexible models:
some open problems
Andrew Gelman
Department of Statistics and Department of Political Science, Columbia
University
A challenge in statistics is to construct models
that are structured enough to be able to learn from data but not be so
strong as to overwhelm the data. We introduce the concept of "weakly
informative priors" which contain important information but less than may be
available for the given problem at hand. We also discuss some related
problems in developing general models for taxonomies and deep interactions.
We consider how these ideas apply to problems in social science and public
health.
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