Dr. Paul-Christian Bürkner
Aalto University, Finland
Towards a Principled Bayesian Workflow
Sonderforschungsbereich 876 "Verfügbarkeit von Information durch Analyse unter Ressourcenbeschränkung"
Prof. Dr. Katharina Morik
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.