Speaker
Description
Introductory statistics classes teach the value of basic plots to diagnose problems with regression models. Like most of us, I found these very boring and spent a long time avoiding them. Now that I focus on complex Bayesian models, I find visualisation of variables, predictions and residuals more useful than ever. Analysts need to track down problems where the model is not fitting the data well, and also where the sampling algorithm is performing poorly, where the priors may be a bad choice, and where the likelihood may be a bad choice. In addition to this, Bayesian models fitted with simulation are fertile ground for visual communication of findings. In this talk, I will review relevant general principles of effective visualisation, recent work on Bayesian workflow, and the role that interactive graphics have to play.
