rlabbe/Kalman-and-Bayesian-Filters-in-Python ? reverse-engineered prompt

Build me a runnable Python notebook project for a friendly introduction to Kalman and Bayesian filters.

I want the whole book to open in Jupyter and let me run the examples, edit the code, and see the plots update. Please make sure the notebooks cover the main topics from the book, like simple filters, discrete Bayes, Gaussians, Kalman filters, nonlinear filtering, unscented and extended Kalman filters, particle filters, smoothing, and the appendix material. Include the supporting code used by the notebooks, plus the figures and any animation assets so the examples work end to end.

Set it up so someone can install the dependencies easily and start reading locally or in a notebook environment without hunting around for missing pieces. If there are any notebooks or helper scripts that need cleanup to make the project easier to use, do that too. Keep the tone and style focused on learning by doing, with examples and exercises that actually run. If you need to check current notebook or plotting docs online, go ahead.