daviddoji/Kalman-and-Bayesian-Filters-in-Python
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Fix - sentence was italic, wanted it to be in bold.

Browse Source
This commit is contained in:
Roger Labbe 2014-08-31 15:48:33 -07:00
parent e7ad67f8bc
commit fb377d75d9
1 changed files with 1 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
README.md
View File

@ -8,7 +8,7 @@ There are multiple ways to read this book. However, it is intended to be interac
The github pages for this project are at http://rlabbe.github.io/Kalman-and-Bayesian-Filters-in-Python/
If you do not want to do that you can read this book online. the website [nbviewer]http://nbviewer.org provides an IPython Notebook server that renders a notebook stored at github (or elsewhere). The rendering is done in real time when you load the book. If you read my book today, and then I make a change tomorrow, when you go back tomorrow you will see that change.
*note: nbviewer is not currently rendering Latex on my or anyone's notebooks, so this is currently a poor option.*
_note: nbviewer is not currently rendering Latex on my or anyone's notebooks, so this is currently a poor option._
You may access this book via nbviewer at any by using this address:
http://nbviewer.ipython.org/github/rlabbe/Kalman-Filters-and-Random-Signals-in-Python/blob/master/Introduction.ipynb
@ -16,12 +16,6 @@ http://nbviewer.ipython.org/github/rlabbe/Kalman-Filters-and-Random-Signals-in-P
Finally, you may generate output in a variety of formats. I will not cover how to do that, other than to point you to [IPython nbconvert](http://ipython.org/ipython-doc/rel-1.0.0/interactive/nbconvert.html). You can convert this book into static HTML pages, latex, or PDF. While I don't recommend it particularly, it is useful for those that don't want to program and/or are working offline.
The github pages for this project are at http://rlabbe.github.io/Kalman-and-Bayesian-Filters-in-Python/
#[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.
author's note: The chapter on g-h filters is fairly complete as far as planned content goes. The content for the discrete Bayesian chapter, chapter 2, is also fairly complete. After that I have questions in my mind as to the best way to present the statistics needed to understand the filters. I try to avoid the 'dump a sememster of math into 4 pages' approash of most textbooks, but then again perhaps I put things off a bit too long. In any case, the subsequent chapters are due a strong editting cycle where I decide how to best develop these concepts. Otherwise I am pretty happy with the content for the one dimensional and multidimensional Kalman filter chapters. I know the code works, I am using it in real world projects at work, but there are areas where the content about the covariance matrices is pretty bad. The implementation is fine, the description is poor. Sorry. It will be corrected.