Kalman Filter

The Kalman Filter arises in the filtering equations of the NDLM in the course on Bayesian time series.

One of the mysteries of the NDLM is how the Matrix G takes it form. This is not explained very well in the course that carries on as if the Kalman filter does not exist. However most of what student find difficult to understand in the NDLM is explained by a quick introduction to the the Kalman filter.

• 95% the material for Gregory Plett’s Kalman filter bootcamp. (Occasional I made minor changes) as well as my own insights from the NDLM. also see Lecture notes and recordings for ECE5550: Applied Kalman Filtering
• Insights relating DLM and Kalman filters from (Petris et al. 2009),
• I plan to will likely also add some insights from (Särkkä 2013)
• Once I complete the bootcamp I be better able to reduced the material further or migrate it to a separate tome!

Petris, G., S. Petrone, and P. Campagnoli. 2009. Dynamic Linear Models with r. Use r! Springer New York. https://link.springer.com/book/10.1007/b135794.

Särkkä, S. 2013. Bayesian Filtering and Smoothing. Bayesian Filtering and Smoothing. Cambridge University Press. https://books.google.co.il/books?id=5VlsAAAAQBAJ.