Due to the Corona-Virus pandemic, this talk is canceled and will be postponed to later this year.

Towards a Principled Bayesian Workflow

Abstract:
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.

Short bio:
Dr. Paul Bürkner is a statistician currently working as a postdoc at Aalto University (Finland), Department of Computer Science. Previously, he has studied Psychology and Mathematics at the Universities of Münster and Hagen and did his PhD about optimal design and Bayesian data analysis at the University of Münster. As a member of the Stan development team and author of the R package brms, a lot of Paul’s work is dedicated to the development and application of Bayesian methods. Specifically, he works on a Bayesian workflow for data analysis that guides researchers and practitioners from the design of their studies to the final decision-making process using state-of-the-art Bayesian statistical methods.

https://paul-buerkner.github.io/about/