diff --git a/14_Smoothing/14_Smoothing.ipynb b/14_Smoothing/Smoothing.ipynb
similarity index 100%
rename from 14_Smoothing/14_Smoothing.ipynb
rename to 14_Smoothing/Smoothing.ipynb
diff --git a/README.md b/README.md
index 2ec117b..d70053b 100644
--- a/README.md
+++ b/README.md
@@ -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**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing/14_Smoothing.ipynb)
+* [**Chapter 14: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing/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.
     
diff --git a/merge_book.py b/merge_book.py
index 5300119..45de288 100644
--- a/merge_book.py
+++ b/merge_book.py
@@ -54,6 +54,8 @@ if __name__ == '__main__':
          '09_Extended_Kalman_Filters/Extended_Kalman_Filters.ipynb',
          '10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb',
          '11_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb',
+         '12_HInfinity_Filters/HInfinity_Filters.ipynb',
+         '14_Smoothing/Smoothing.ipynb',
          'Appendix_A_Installation/Appendix_Installation.ipynb',
          'Appendix_B_Symbols_and_Notations/Appendix_Symbols_and_Notations.ipynb'])
 
diff --git a/table_of_contents.ipynb b/table_of_contents.ipynb
index dbde96f..d249c07 100644
--- a/table_of_contents.ipynb
+++ b/table_of_contents.ipynb
@@ -97,7 +97,7 @@
       "\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**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing/14_Smoothing.ipynb)\n",
+      "[**Chapter 14: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing/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",