When the data that you’re modelling naturally splits into sectors — like countries, branches of a store, or different hospitals within a region – it’s difficult to decide whether you should model jointly or separately. Modelling jointly takes advantage of all the data available, but ignores the subtleties that distinguish each individual sector. On the other hand, fitting a separate model to each sector is prone to overfitting, especially where there’s limited data.
However, an approach called hierarchical modelling can combine the best of both approaches, allowing us to take advantage of the overarching information across sectors without giving up on their distinctive features.
In this talk, Faculty Data Scientist Omar Sosa will provide an introduction to the approach, focusing heavily on its practical side. He’ll cover:
- When hierarchical modelling can be used.
- How to implement hierarchical modelling.
- The limitations of using this approach.
- What can we learn by implementing hierarchical modelling.
You’ll need some familiarity with Bayesian inference to get the most value from this talk.