In both models, there&#x27;s an unobserved state that changes over time according to relatively simple rules, and you get indirect information about that state every so often.<p>In Kalman filters, you assume the unobserved state is Gaussian-ish and it moves continuously according to linear-ish dynamics (depending on which flavor of Kalman filter is being used).<p>In HMMs, you assume the hidden state is one of a few classes, and the movement among these states uses a discrete Markov chain.<p>In my experience, the algorithms are often pretty different for these two cases, but the underlying idea is very similar.