From 40e904a4fe4f0d7fc57558e6eec5b7b417643900 Mon Sep 17 00:00:00 2001 From: Thomas Feld Date: Thu, 29 Oct 2015 09:17:56 +0100 Subject: [PATCH] Update table_of_contents.ipynb Fixed path of chapter 14 (previously 13?) --- table_of_contents.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/table_of_contents.ipynb b/table_of_contents.ipynb index d3eecd5..cc7feb3 100644 --- a/table_of_contents.ipynb +++ b/table_of_contents.ipynb @@ -79,7 +79,7 @@ "Kalman filters are recursive, and thus very suitable for real time filtering. However, they work extremely well for post-processing data. After all, Kalman filters are predictor-correctors, and it is easier to predict the past than the future! We discuss some common approaches.\n", "\n", "\n", - "[**Chapter 14: Adaptive Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/13-Adaptive-Filtering.ipynb)\n", + "[**Chapter 14: Adaptive Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14-Adaptive-Filtering.ipynb)\n", " \n", "Kalman filters assume a single process model, but manuevering targets typically need to be described by several different process models. Adaptive filtering uses several techniques to allow the Kalman filter to adapt to the changing behavior of the target.\n", "\n",