(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
I’ll be speaking at the workshop on Data-Efficient Machine Learning. And here’s the schedule.
I’ll be speaking on the following topic:
Toward Routine Use of Informative Priors
Bayesian statistics is typically performed using noninformative priors but the resulting inferences commonly make no sense and also can lead to computational problems as algorithms have to waste time in irrelevant regions of parameter space. Certain informative priors that have been suggested don’t make much sense either. We consider some aspects of the open problem of using informative priors for routine data analysis.
I’ll also be speaking on one other, related, thing. And I’ll be part of the panel discussion at 3:30pm.
The workshop will be at the Marriott Marquis (Astor Room), New York.
P.S. The slides are here.
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