Creating structured and flexible models: some open problems

October 8, 2010
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(This article was first published on VCASMO - drewconway, and kindly contributed to R-bloggers)

Prof. Andrew Gelman, from both the Statistics and Political Science departements at Columbia presented this talk to the New York R Statistical Programming Meetup on October 7, 2010.

Description: 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. All the work for these projects was done in R.

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