Title: Towards a Principled Bayesian Workflow
Speaker: Paul-Christian Buerkner, Aalto University, Finland
When: 2020-05-14 16:15
Where: OH14, E023
Probabilistic programming languages such as Stan, which can be used to specify and fit Bayesian models, have revolutionized the practical application of Bayesian statistics. They are an integral part of Bayesian data analysis and provide the basis for obtaining reliable and valid inference. However, they are not sufficient by themselves. Instead, they have to be combined with substantive statistical and subject matter knowledge, expertise in programming and data analysis, as well as critical thinking about the decisions made in the process. A principled Bayesian workflow for data analysis consists of several steps from the design of the study, gathering of the data, model building, estimation, and validation, to the final conclusions about the effects under study. I want to present a concept for an interactive Bayesian workflow which helps users by diagnosing problems and giving recommendations for sensible next steps. This concept gives rise to a lot of interesting research questions we want to investigate in the upcoming years.