Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy

Understand

Probabilistic (Bayesian) modeling has experienced a surge of applications in almost all quantitative sciences and industrial areas.

• This development is driven by a combination of several factors, including better probabilistic estimation algorithms, flexible software, increased computing power, and a growing awareness of the benefits of probabilistic learning.
• However, a principled Bayesian model building workflow is far from complete and many challenges remain.
• To aid future research and applications of a principled Bayesian workflow, we ask and provide answers for what we perceive as two fundamental questions of Bayesian modeling, namely (a) "What actually is a Bayesian model?" and (b) "What makes a good Bayesian model?".