Merge pull request CamDavidsonPilon#213 from PsychoCoderHC/master

additions and grammer typo fix

Author: CamDavidsonPilon

Chapter1_Introduction/README.md

@@ -1 +1,4 @@
-[Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Chapter1_Introduction.ipynb)
+Chapter 1: Introduction to Bayesian Methods
+===========
+
+### [Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Chapter1_Introduction.ipynb)

Chapter2_MorePyMC/README.md

@@ -1 +1,4 @@
-[Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/MorePyMC.ipynb)
+Chapter 2: A little more on PyMC
+=================================
+
+### [Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/MorePyMC.ipynb)

Chapter3_MCMC/README.md

@@ -1 +1,4 @@
-[Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/IntroMCMC.ipynb)
+Chapter 3: Opening the black box of MCMC (Markov Chain Monte Carlo)
+==================================================================
+
+### [Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/IntroMCMC.ipynb)

Chapter3_MCMC/github_pull.py

@@ -21,7 +21,6 @@
 from requests import get
 
 
-
 MAX = 8000000
 today =  datetime.datetime.today()
 randint = np.random.randint
@@ -56,8 +55,6 @@
             is_valid_language = True
         except:
             pass
-        
-    
 
     #languages 
     X[ i, language_mappings[ repo_results["language" ] ] ] = 1
@@ -84,6 +81,3 @@
     print 
 
 np.savetxt("data/github_data.csv", X, delimiter=",", fmt="%d" )
-    
-
-

Chapter4_TheGreatestTheoremNeverTold/LawOfLargeNumbers.ipynb

@@ -480,7 +480,7 @@
       "One way to determine a prior on the upvote ratio is to look at the historical distribution of upvote ratios. This can be accomplished by scraping Reddit's comments and determining a distribution. There are a few problems with this technique though:\n",
       "\n",
       "1. Skewed data:  The vast majority of comments have very few votes, hence there will be many comments with ratios near the extremes (see the \"triangular plot\" in the above Kaggle dataset), effectively skewing our distribution to the extremes. One could try to only use comments with votes greater than some threshold. Again, problems are encountered. There is a tradeoff between number of comments available to use and a higher threshold with associated ratio precision. \n",
-      "2. Biased data: Reddit is composed of different subpages, called subreddits. Two examples are *r/aww*, which posts pics of cute animals, and *r/politics*. It is very likely that the user behaviour towards comments of these two subreddits are very different: visitors are likely friendly and affectionate in the former, and would therefore upvote comments more, compared to the latter, where comments are likely to be controversial and disagreed upon. Therefore not all comments are the same. \n",
+      "2. Biased data: Reddit is composed of different subpages, called subreddits. Two examples are *r/aww*, which posts pics of cute animals, and *r/politics*. It is very likely that the user behaviour towards comments of these two subreddits are very different: visitors are likely to be more friendly and affectionate in the former, and would therefore upvote comments more, compared to the latter, where comments are likely to be controversial and disagreed upon. Therefore not all comments are the same. \n",
       "\n",
       "\n",
       "In light of these, I think it is better to use a `Uniform` prior.\n",

Chapter4_TheGreatestTheoremNeverTold/README.md

@@ -1 +1,3 @@
-[Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter4_TheGreatestTheoremNeverTold/LawOfLargeNumbers.ipynb)
+Chapter 4: The Greatest Theorem Never Told
+====
+### [Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter4_TheGreatestTheoremNeverTold/LawOfLargeNumbers.ipynb)

Chapter5_LossFunctions/README.md

@@ -1 +1,4 @@
-[Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter5_LossFunctions/LossFunctions.ipynb)
+Chapter 5: Would you rather lose an arm or a leg?
+====
+
+### [Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter5_LossFunctions/LossFunctions.ipynb)

Chapter6_Priorities/README.md

@@ -1 +1,4 @@
-[Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/Priors.ipynb)
+Chapter 6: Getting our priorities straight
+===========
+
+### [Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/Priors.ipynb)