diff --git a/table_of_contents.ipynb b/table_of_contents.ipynb
index b48ccd5..c34aeca 100644
--- a/table_of_contents.ipynb
+++ b/table_of_contents.ipynb
@@ -1,7 +1,7 @@
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@@ -65,42 +65,50 @@
       "Building on material in Chapter 6, walks you through the design of several Kalman filters. Discusses, but does not solve issues like numerical stability.\n",
       "\n",
       "\n",
-      "[**Chapter 9: Extended Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/09_Extended_Kalman_Filters.ipynb)\n",
+      "[**Chapter 9: Nonlinear Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/09_Nonlinear_Filtering.ipynb)\n",
       "\n",
-      "Kalman filter as covered only work for linear problems. Extended Kalman filters (EKF) are the most common approach to linearizing non-linear problems.\n",
+      "Kalman filter as covered only work for linear problems. Here I introduce the problems that nonlinear systems pose to the filter, and briefly discuss the various algorithms that we will be learning in subsequent chapters which work with nonlinear systems.\n",
       "\n",
-      "*Still very early going on this chapter.*\n",
       "\n",
       "[**Chapter 10: Unscented Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/10_Unscented_Kalman_Filter.ipynb)\n",
       "\n",
       "Unscented Kalman filters (UKF) are a recent development in Kalman filter theory. They allow you to filter nonlinear problems without requiring a closed form solution like the Extended Kalman filter requires.\n",
       "\n",
-      "[**Chapter 11: Ensemble Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/11_Ensemble_Kalman_Filters.ipynb)\n",
       "\n",
-      "Discusses the ensemble Kalman Filter, which uses a Monte Carlo approach to deal with very large Kalman filter states in nonlinear systems.\n",
+      "[**Chapter 11: Extended Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/11_Extended_Kalman_Filters.ipynb)\n",
+      "\n",
+      "Kalman filter as covered only work for linear problems. Extended Kalman filters (EKF) are the most common approach to linearizing non-linear problems.\n",
+      "\n",
+      "*Still very early going on this chapter.*\n",
+      "\n",
       "\n",
       "[**Chapter 12: Designing Nonlinear Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/12_Designing_Nonlinear_Kalman_Filters.ipynb)\n",
       "\n",
       "Works through some examples of the design of Kalman filters for nonlinear problems. *This is still very much a work in progress.*\n",
       "\n",
       "\n",
-      "[**Chapter 13: H-Infinity Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/13_HInfinity_Filters.ipynb)\n",
+      "[**Chapter 13: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/13_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",
+      "\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",
+      "\n",
+      "[**Chapter 15: H-Infinity Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/15_HInfinity_Filters.ipynb)\n",
       "    \n",
       "Describes the $H_\\infty$ filter. \n",
       "\n",
       "*I have code that implements the filter, but no supporting text yet.*\n",
       "\n",
       "\n",
-      "[**Chapter 14: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing.ipynb)\n",
+      "[**Chapter 16: Ensemble Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/16_Ensemble_Kalman_Filters.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",
+      "Discusses the ensemble Kalman Filter, which uses a Monte Carlo approach to deal with very large Kalman filter states in nonlinear systems.\n",
       "\n",
       "\n",
-      "[**Chapter 15: Adaptive Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/15_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",
-      " \n",
       "[**Chapter XX: Numerical Stability**](not implemented)\n",
       "\n",
       "EKF and UKF are linear approximations of nonlinear problems. Unless programmed carefully, they are not numerically stable. We discuss some common approaches to this problem.\n",