#[Kalman Filters and Random Signals in Python](http://github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python)

### Version 0.0 - not ready for public consumption. In development.

this is a book BLAH BLAH BLAH

Contents
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* [**Introduction**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python/blob/master/Introduction.ipynb)

    Introduction to the Kalman filter. Explanation of the idea behind this book.


* [**Chapter 1: The g-h Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python/master/g-h_filter.ipynb)
    Intuitive introduction to the g-h filter, which is a family of filters that includes the Kalman filter. Not filler - once you understand this chapter you will understand the concepts behind the Kalman filter. 


* [**Chapter 2: The Discrete Bayes Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python/blob/master/histogram_filter.ipynb)
    Introduces the Discrete Bayes Filter. From this you will learn the probabilistic reasoning that underpins the Kalman filter in an easy to digest form.

* [**Chapter 3: Gaussian Probabilities**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python/blob/master/Gaussians.ipynb)
    Introduces using Gaussians to represent beliefs. Gaussians allow us to implement the algorithms used in the Discrete Bayes Filter to work in continuous domains.

* [**Chapter 4: One Dimensional Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python/blob/master/Kalman_Filters.ipynb)
    Implements a Kalman filter by modifying the Discrete Bayesian Filter to use Gaussians. This is a full featured Kalman filter, albeit only useful for 1D problems. 

* [**Chapter 5: Multidimensional Kalman Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python/blob/master/Multidimensional_Kalman_Filters.ipynb)
    We extend the Kalman filter developed in the previous chapter to the full, generalized filter. 


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