Thanks a lot for the response! Oh yes, that makes sense. So let’s say I generate the data using the below dynamics
X_t = AX_{t-1} + N(0,\sigma^2)
but in the PyMC, I define the model to be a random walk. So the posterior estimates would be super accurate even though the model I have specified is inaccurate, is my understanding right? Is there a way to infer that my model specification is bad? One way would be to say in the model that X_t is not a random variable, but a deterministic one given the initial state and \sigma^2, then I can estimate how far off are these deterministic states from the true value, does that make sense?