Kalman and Bayesian Filters in Python

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, and more. All exercises include solutions. Introductory text for Kalman and Bayesian filters. All code is written in Python, and the book itself is written using Juptyer Notebook so that you can run and modify the code in your browser. What better way to learn? This book teaches you how to solve all sorts of filtering problems. Use many different algorithms, all based on Bayesian probability. In simple terms Bayesian probability determines what is likely to be true based on past information. This book is interactive. While you can read it online as static content, it's better to use it as intended. It is written using Jupyter Notebook, which allows you to combine text, math, Python, and Python output in one place.