Kalman-and-Bayesian-Filters-in-Python
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rlabbe
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters...
In chapter 8 (Evaluating Filter Performance - NEES section) it says: > This is an excellent measure of the filter's performance, and should be used whenever possible, especially in production...
In the "The Discrete Bayes Algorithm" section the predict bubble has an arrow coming out of it labelled 'estimated state', with x_hat. This is not right. The output is the...
For example, I wrote the function G=gaussian, and norm_cdf. Are these really necessrary, given the frozen rvs supported by SciPy. Even if SciPy is slightly less useful for my purposes,...
I don't recall if I did this or not - but both F and Q need to be recomputed when the data rate is varying.
Docstrings are needed so that the ? notebook magic gives useful information to the reader. Plus, document all the things!!!
I wrote this when SciPy did not support this function. It has now been supported for 2 years. Better to have people learn SciPy than my library. this requires removing...
I mention it in the Gaussians chapter, and perhaps use it in a place or two, but wouldn't it be better to use it more uniformly throughout the book?
I believe there's an error in the section Design the System Model for the robot localization example. The motion model is actually providing a relationship between the past and future...
Not sure why this cell was here. It doesn't appear to have anything to do with the rest of the notebook.
In Chapter 3 in the last sentence of the first paragraph of the section **Total Probability Theorem** i think it should say posterior instead of prior. The probability of being...
