Series: The Learn Arc — 50 posts through the Active Inference workbench.

Previous: Part 45 — Session §9.2: Comparing models

Hero line. One subject. One dataset. Two candidate models. One posterior per model, one Bayes factor on top. The whole Chapter 9 pipeline fits on one screen — and once you have walked it end to end, the methodology stops feeling like magic.

Why a case study closes Chapter 9

The mathematics in 9.1 and 9.2 is self-similar enough that it is easy to agree with and hard to feel. Session 9.3 collapses the whole chain into a single concrete example so you can watch the decisions happen: which parameters are free, which priors you set, which model wins, and how strongly.

Five beats

The subject is a simulated agent with known ground truth. Because we control the generator, we know the right answer. That makes the case study diagnostic — if the pipeline does not recover it, the bug is in the pipeline, not the subject.

Two models: flat vs hierarchical.…