README

Author: CamDavidsonPilon

README.md

@@ -21,31 +21,31 @@ Contents
 
 (This is all rendered via the *nbviewer* is is read-only. Editable notebooks + examples can be downloaded too by forking! )
 
-1. [Chapter 1: Introduction to Bayesian Methods](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Chapter1_Introduction.ipynb)
-    Introduction to the philosophy and practice of Bayesian methods and answering the question "What is probabilistic programming?" 
-    1. Example: Inferring human behaviour changes from text message rates.
+1. [**Chapter 1: Introduction to Bayesian Methods**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Chapter1_Introduction.ipynb)
+    Introduction to the philosophy and practice of Bayesian methods and answering the question "What is probabilistic programming?" Examples include:
+    - Inferring human behaviour changes from text message rates.
 
-2. [Chapter 2: A little more on PyMC](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/MorePyMC.ipynb)
-    We explore modeling Bayesian problems using Python's PyMC library through examples. How do we create Bayesian models?
-    1. Example: Definitive linking between smoking and death.
-    2. Example: Calculating probabilities of space-shuttle disasters.
+2. [**Chapter 2: A little more on PyMC**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/MorePyMC.ipynb)
+    We explore modeling Bayesian problems using Python's PyMC library through examples. How do we create Bayesian models? Examples include:
+    - Definitive linking between smoking and death.
+    - Calculating probabilities of space-shuttle disasters.
 
-3. [Chapter 3: Opening the Black Box of MCMC](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/IntroMCMC.ipynb)
+3. [**Chapter 3: Opening the Black Box of MCMC**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/IntroMCMC.ipynb)
     We discuss how MCMC operates and diagnostic tools.
 
-4. [Chapter 4: The Greatest Theorem Never Told](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter4_TheGreatestTheoremNeverTold/LawOfLargeNumbers.ipynb)
-    We explore an incredibly useful, and dangerous, theorem: The Law of Large Numbers
-    1. Example: Exploring a Kaggle dataset and the pitfalls of naive analysis
+4. [**Chapter 4: The Greatest Theorem Never Told**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter4_TheGreatestTheoremNeverTold/LawOfLargeNumbers.ipynb)
+    We explore an incredibly useful, and dangerous, theorem: The Law of Large Numbers. Examples include:
+    - Exploring a Kaggle dataset and the pitfalls of naive analysis
 
-5. [Chapter 5: Would you rather loss an arm or a leg?](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter5_/LossFunctions.ipynb)
-    The introduction of Loss functions and there (awesome) use in Bayesian methods. 
-    1. Example: Solving the Price is Right's Showdown
-    2. Example: Optimizing financial predictions
-    3. Example: Winning solution to the Kaggle Dark World's competition.
+5. [**Chapter 5: Would you rather loss an arm or a leg?**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter5_/LossFunctions.ipynb)
+    The introduction of Loss functions and there (awesome) use in Bayesian methods.  Examples include:
+    - Example: Solving the Price is Right's Showdown
+    - Example: Optimizing financial predictions
+    - Example: Winning solution to the Kaggle Dark World's competition.
 
 10\. Chapter 10: More PyMC Hackery
-    We explore the gritty details of PyMC through code and examples.
-    1. Example: Analysis on real-time GitHub repo stars and forks.
+    We explore the gritty details of PyMC through code and examples. Examples include:
+    - Example: Analysis on real-time GitHub repo stars and forks.
 
 
 Using the book