When the dynamics model and the observation model of a state space model are both Gaussian, that is we have a linear gaussian ssm, we can perform inference efficiently using Kalman filtering methods.
We perform filtering to predict one step and obtain
- Time update step
- Measurement step, getting the expected observation
The algorithm scales as
When all the data have arrived, we can perform offline smoothing and obtain