Flaky Tests Question

Flaky tests, such as [test_conditional_prob_inf_given_vl_dist](https://github.com/CERN/CAiMIRA/blob/master/caimira/tests/test_conditional_probability.py#L47C16-L47C16), are due to their non-deterministic nature. Monte Carlo simulations, such as those used in the `baseline_exposure_model` fixture and `test_conditional_prob_inf_given_vl_dist`, fail because they're probabilistic. For modelling this is great - but for unit tests its not 😅 It's a nice feeling to have a 🟢 CI.

All you'd have to do to fix this test is set the random seed, like `np.random.seed(42)`. However, this is misleading if the goal (as I suspect) is to ensure the models accuracy within a certain tolerance.

If the goal _is_ to measure the models accuracy, what if you removed the 3x retry and instead ran the model 10 times, gathering the results each time? Then you could do a statistical analysis against the mean/median/standard deviation/percentiles etc.

Another alternative is to change the fixed `0.002` tolerance. What if you calculated the tolerance based on the number of runs? Could an absolute tolerance of `0.002` be wishful thinking?

It could also help to log model deviations and store them with a timestamp. Then you can monitor deviations over time.

I'm happy to fix this test (and get that CI ✅). Its a cool project. Just let me know what the expected behavior is & how I can help out.

Start in caimira/tests/test_conditional_probability.py at test_conditional_prob_inf_given_vl_dist and inspect the baseline_exposure_model fixture, including the three retries and 0.002 tolerance. First clarify whether the goal is deterministic unit coverage or statistical accuracy, then run the targeted test repeatedly to observe its failures. Done requires an agreed testing approach and consistently passing CI.

Written by the indexing model from the issue text.