Presentation: Bayesian Workflow
Speaker: Andrew Gelman, Ph.D., Higgens Professor of Statistics, Professor of Political Science, and Director, Applied Statistics Center, Columbia University
Abstract: Bayesian inference is typically explained in terms of fitting a particular model to a particular dataset. But this sort of model fitting is only a small part of real-world data analysis. In this talk we consider several aspects of workflow that have not been well served by traditional Bayesian theory, including scaling of parameters, weakly informative priors, predictive model evaluation, variable selection, model averaging, checking of approximate algorithms, and frequency evaluations of Bayesian inferences. We discuss the application of these ideas in various applications in social science and public health.