Machine-Assisted Bayesian Workflow

Building Bayesian models in a principled way remains a highly complex task requiring a lot of expertise and cognitive resources. Ideally, subject matter experts do not have to solve everything by themselves but have statisticians or data scientists by their side to assist them.

Of course, the latter are not always available for every data-analysis project. As a remedy we are developing a machine-assisted workflow for building interpretable, robust, and well-predicting Bayesian models. This first requires more research on the theoretical foundations of Bayesian model building. With this in hand, machines will be trained to provide automatic model evaluation and modeling recommendations that guide the user through the model building process.

While leaving the modeling choices up to the user, the machine subsequently learns from the user’s decisions to improve its recommendations on the fly.