An interactive jupyter notebook to illustrate Kalman-Bucy filter.
This is a course project for the course Stochastic Processes and Applications, in 2018 Fall, at University of Wyoming. So it is heavily written in Stochastic language and notations taught in that course. But the interactive plots and the notes directly after the plots could be helpful in understanding what filtering and data assimilation is about.
You can use RISE to convert the notebook into slides.
Run a discrete square dynamic interactively
A continuous circular dynamic interactively
And plot the Kalman Filtering process
Instead of running it locally, I'd suggest open it on Binder, by just clicking this link.
Or, if you insist, use the requirements.txt to build dependencies:
pip install -r requirements.txt
But there are no fancy requirements. If all the packages below are installed, you should be good to go:
- jupyter
- bqplot
- scipy
- numpy