# ssm-book
Executable textbook on state-space models, to accompany the [ssm-jax](https://github.com/probml/ssm-jax) library.
The rendered content can be found at [https://probml.github.io/ssm-book/root.html](https://probml.github.io/ssm-book/root.html).
Some of this material is also covered in my book [Probabilistic Machine Learning: Advanced Topics](https://probml.github.io/pml-book/book2.html)


Authors: Kevin Murphy, Scott Linderman, et al. MIT License. 2022



Related books:
- [Bayesian filtering and smoothing](https://users.aalto.fi/~ssarkka/pub/cup_book_online_20131111.pdf), Simo Sarkka, 2013. [Matlab code](https://www.cambridge.org/us/academic/subjects/statistics-probability/applied-probability-and-stochastic-networks/bayesian-filtering-and-smoothing?format=HB), [Numpy code](https://github.com/EEA-sensors/Bayesian-Filtering-and-Smoothing/tree/main/python), [Jax code](https://github.com/petergchang/sarkka-jax)
- [State estimation for robotics](http://asrl.utias.utoronto.ca/~tdb/bib/barfoot_ser17.pdf), Tim Barfoot, 2017.
- [Kalman and Bayesian filters in Python](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python), Roger Labbe, 2015. [Python code](https://github.com/rlabbe/filterpy)
