Just to touch on one part of your question -- did you see the case study section? There are five conclusions: > 1. This approach provides a systematic process

Just to touch on one part of your question -- did you see the case study section? There are five conclusions: > 1. This approach provides a systematic process of developing bespoke models tailored to our specific problem. > 2. It provides transparency to our model as we explicitly defined our model assumptions by leveraging prior knowledge about traffic congestion. > 3. The approach allows handling of uncertainty in a principled manner using probability theory. > 4. It does not suffer from overfitting as the model parameters are learned using Bayesian inference and not optimization. > 5. Finally, MBML separates the model development from inference which allows us to build several models and use the same inference algorithm to learn the model parameters. This in turn helps to quickly compare several alternative models and select the best model that is explained by the observed data.