Kalman smoothing algorithm¶
Kalman filtering updates the belief as the data arrives.
Kalman smoothing computes the posterior:
\[p(z_t| y_{1:t})\]
after we observed all the data, this reduces the posterior uncertainty significantly.
It is analogous to the forward-backward algorithm in HMM, with some small differences.