Statistics and Data Science Seminar

— 3:15pm

Location:
In Person - Doherty Hall A302

Speaker:
ANDREW GELMAN, Higgins Professor of Statistics and Professor of Political Science, Departments of Statistics and Political Science, Columbia University
http://www.stat.columbia.edu/~gelman/

Bayesian Workflow: Some Progress and Open Questions

The workflow of applied Bayesian statistics includes not just inference but also model building, model checking, confidence-building using fake data, troubleshooting problems with computation, model understanding, and model comparison.  We would like to toward codify these steps in the realistic scenario in which researchers are fitting many models for a given problem.  We discuss various issues including prior distributions, data models, and computation, in the context of ideas such as the Fail Fast Principle and the Folk Theorem of Statistical Computing.  We also consider some examples of Bayesian models that give bad answers and see if we can develop a workflow that catches such problems.  Additional background. 

⇒  Meet and Greet with Andrew:  1:45-2:05 pm - Baker Hall 129 (with snacks) (prior to the talk) 

— 

Andrew Gelman is a professor of statistics and political science at Columbia University. He has received the Outstanding Statistical Application award three times from the American Statistical Association, the award for best article published in the American Political Science Review, the Mitchell and DeGroot prizes from the International Society of Bayesian Analysis, and the Council of Presidents of Statistical Societies award. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin), Teaching Statistics: A Bag of Tricks (with Deborah Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina), and Regression and Other Stories (with Jennifer Hill and Aki Vehtari). 

Andrew has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; the effects of incumbency and redistricting; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.

Event Website:
https://www.cmu.edu/dietrich/statistics-datascience/events/index.html