# Good book for intuition: Kalman and Bayesian Filters in Python **URL:** \ **Category:** Dynamax\ **Created:** [January 24, 2023, 6:12am UTC](https://community.intuitivebayes.com/t/good-book-for-intuition-kalman-and-bayesian-filters-in-python/902 "2023-01-24T06:12:51Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![charlie](https://yyz1.discourse-cdn.com/flex007/user_avatar/community.intuitivebayes.com/charlie/32/445_2.png) [@charlie](https://community.intuitivebayes.com/u/charlie)\ **Post date:** [January 24, 2023, 6:12am UTC](https://community.intuitivebayes.com/t/good-book-for-intuition-kalman-and-bayesian-filters-in-python/902/1 "2023-01-24T06:12:51Z")
I’ve found this book by Roger R. Labbe Jr to be helpful in getting intuition for Kalman filters and other kinds of state-space models. It is freely available online as Jupyter notebooks and a PDF: > **[GitHub - rlabbe/Kalman-and-Bayesian-Filters-in-Python: Kalman Filter book...](https://github.com/rlabbe/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 filt... This book has lots of examples and tries to simplify the notation. Kevin Murphy’s book jumps right to results and has exhaustive notation that can be overwhelming; if you want a gentler introduction, especially as a warm-up to reading chapter 8 of Murphy’s Advanced Topics book, I’d recommend this book. ---
**Author:** ![patel](https://avatars.discourse-cdn.com/v4/letter/p/dbc845/32.png) [@patel](https://community.intuitivebayes.com/u/patel)\ **Post date:** [February 1, 2023, 2:27am UTC](https://community.intuitivebayes.com/t/good-book-for-intuition-kalman-and-bayesian-filters-in-python/902/2 "2023-02-01T02:27:33Z")
Another great book for intuition is Artificial Intelligence: A Modern Approach. Both 3rd and 4th edition are well-written