This is a very educational and intuitive way of putting it, but to nitpick the Kalman filter is a very special case of this (it assumes a linear ruleset and Gaussian uncertainties in the sensor readings).
What you're describing is in general the Bayesian filter (or Bayesian smoothing if you don't have to give the result immediately).
Yep, that's right. I thought about adding that detail but decided it might detract from the main points. Hopefully anyone interested also sees your comment.
What you're describing is in general the Bayesian filter (or Bayesian smoothing if you don't have to give the result immediately).