Implemented CI-based unit tests #126
Description
Ran into an issue today with one of the new prototype algorithms (RBPFBSi) where it looked like the posteriors were correct to high tolerance (rtol = 1e-3) but they were actually incorrect. This was only spotted when I whacked N_sample and N_particles up to massive numbers to test the runtime.
I noticed that this false negative could have been avoid if I had used the std of the mean estimates generated by the RBPF to see whether the true Kalman mean was within a 95% confidence interval. This is probably the better way to write tests going forward as it allows us to avoid setting arbitrary rtols and instead be model-driven.