Learning Poisson State Space Model parameters for large number of samples

Your original approach might be right, I’ve just never seen it coded that way. It seems to me like it’s not propagating uncertainty about each state into the future correctly. A priori, I expected something like this, using a scan to construct latent states. That’s what the pymc-statespace package does (but it runs a special algorithm to try to compute the likelihood faster. It’s unclear if that’s still true following some of the recent upgrades to scan likelihood inference)