daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Start of the Smoothin chapter. RTS implemented.

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Roger Labbe 2014-11-16 15:54:28 -08:00
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14_Smoothing/14_Smoothing.ipynb Normal file
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README.md
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@ -146,7 +146,7 @@ EKF and UKF are linear approximations of nonlinear problems. Unless programmed c
* [**Chapter 13: Numerical Stability**](not implemented)
* [**Chapter 14: Smoothing**](not implemented)
* [**Chapter 14: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing/14_Smoothing.ipynb)
Kalman filters are recursive, and thus very suitable for real time filtering. However, they work well for post-processing data. We discuss some common approaches.

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table_of_contents.ipynb
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@ -97,11 +97,10 @@
"\n",
"*This chapter is not started. I'm likely to rearrange where this material goes - this is just a placeholder.*\n",
"\n",
"[**Chapter 14: Smoothing**](not implemented)\n",
"[**Chapter 14: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing/14_Smoothing.ipynb)\n",
" \n",
"Kalman filters are recursive, and thus very suitable for real time filtering. However, they work well for post-processing data. We discuss some common approaches.\n",
" \n",
"*Not implemented. The filterpy library does contain some smothers, however.*\n",
" \n",
" \n",
"[**Chapter 15: Particle Filters**](not implemented)\n",
" \n",