Andres Barrientos - Colloquium Speaker

Assistant Professor, Department of Statistics, Florida State University
Thursday, April 22, 2021 - 3:15pm
Colloquium Title: 
Differentially private methods for Bayesian model uncertainty in linear regression models


Statistical methods for confidential data are in high demand, for reasons ranging from recent trends in privacy law to ethical considerations. Currently, differential privacy is the most widely adopted formalization of privacy of randomized algorithms in the literature. This article provides differentially private methods for handling model uncertainty in normal linear regression models. More precisely, we introduce techniques that allow us to provide differentially private Bayes factors, posterior probabilities, and model-averaged estimates. Our methods are conceptually simple and easy to run with existing implementations of non-private methods.


Everyone is welcome to join! Please note that the meeting opens at 3:15pm, and the presentation is at 3:30-4:20pm. There will be time afterward for Q&A with the speaker.

Topic: Colloquia: Department of Statistics and Actuarial Science, The University of Iowa

Time: Apr 22, 2020 03:15 PM Central Time (US and Canada)

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Meeting ID: 949 5289 2803