Discussion of Big Bayes Stories and BayesBag

TL;DR

This paper discusses the power of Bayesian inference, highlighting recent contributions that use hierarchical models to make interpretable predictions with uncertainty estimates on global population data.

Contribution

It emphasizes the effectiveness of hierarchical Bayesian models in public interest predictions, showcasing their interpretability and uncertainty quantification.

Findings

Hierarchical models enable clear interpretation of predictions.

Bayesian methods incorporate prior knowledge effectively.

Uncertainty regions are provided for future predictions.

Abstract

I congratulate all the authors for their insightful papers with wide-ranging contributions. The articles demonstrate the power and elegance of the Bayesian inference paradigm. In particular, it allows to incorporate prior knowledge as well as hierarchical model building in a convincing way. Regarding the latter, the contribution by Raftery, Alkema and German is a very fascinating piece, as it addresses a set of problems of great public interest and presents predictions for the world populations and other interesting quantities with uncertainty regions. Their approach is based on a hierarchical model, taking various characteristics into account (e.g., fertility projections). It would have been very difficult to come up with a "better" solution which would be as clear in terms of interpretation (in contrast to a "black-box machine") and which would provide (model-based) uncertainties for the predictions into the future.