Press "Enter" to skip to content

Understanding the Bayesian Nature of Kalman Filters

Holger von Jouanne-Diedrich gives us an interesting interpretation of Kalman filters:

The Kalman filter is a very powerful algorithm to optimally include uncertain information from a dynamically changing system to come up with the best educated guess about the current state of the system. Applications include (car) navigation and stock forecasting. If you want to understand how a Kalman filter works and build a toy example in R, read on!

The following post is based on the post “Das Kalman-Filter einfach erklärt” which is written in German and uses Matlab code (so basically two languages nobody is interested in any more 😉 ). This post is itself based on an online course “Artificial Intelligence for Robotics” by my colleague Professor Sebastian Thrun of Standford University.

In fairness, I regret only one thing about learning German: that I’ve forgotten so much over the years.