Kalman filter

(2.4 hours to learn)


The Kalman filter is an algorithm for inference in linear dynamical systems. Specifically, the task is to infer the posterior over the current latent state given past observations. It forms the basis for approximate inference algorithms in more general state space models.


This concept has the prerequisites:

Core resources (read/watch one of the following)


Supplemental resources (the following are optional, but you may find them useful)


See also

  • Mathematical derivation of the Kalman filter
  • The Kalman smoother can be used to infer the posterior marginals over the whole sequence, not just the current state.
  • Kalman smoothing can be regarded as a speical case of the [forward-backward algorithm](forward_backward_algorithm) for HMMs.