Creating structured and ﬂexible models: some open problems
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If you have a question about this talk, please contact Zoubin Ghahramani.
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 interactions. We consider how these ideas apply to problems in social science and public health. If you don’t walk out of this talk as a Bayesian, I’ll eat my hat.
This talk is part of the Machine Learning @ CUED series.
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