update prolouge links

Author: CamDavidsonPilon

Prologue/Prologue.ipynb

@@ -48,51 +48,38 @@
     "\n",
     "* [**Prologue:**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Prologue/Prologue.ipynb) Why we do it.\n",
     "\n",
-    "* [**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.ipynb)\n",
+    "* [**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/Ch1_Introduction_PyMC3.ipynb)\n",
     "    Introduction to the philosophy and practice of Bayesian methods and answering the question \"What is probabilistic programming?\" Examples include:\n",
     "    - Inferring human behaviour changes from text message rates.\n",
     "    \n",
-    "* [**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/Chapter2.ipynb)\n",
+    "* [**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/Ch2_MorePyMC_PyMC3.ipynb)\n",
     "    We explore modeling Bayesian problems using Python's PyMC library through examples. How do we create Bayesian models? Examples include:\n",
     "    - Detecting the frequency of cheating students, while avoiding liars.\n",
     "    - Calculating probabilities of the Challenger space-shuttle disaster.\n",
     "    \n",
-    "* [**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/Chapter3.ipynb)\n",
+    "* [**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/Ch3_IntroMCMC_PyMC3.ipynb)\n",
     "    We discuss how MCMC operates and diagnostic tools. Examples include:\n",
     "    - Bayesian clustering with mixture models\n",
     "    \n",
-    "* [**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/Chapter4.ipynb)\n",
+    "* [**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/Ch4_LawOfLargeNumbers_PyMC3.ipynb)\n",
     "    We explore an incredibly useful, and dangerous, theorem: The Law of Large Numbers. Examples include:\n",
     "    - Exploring a Kaggle dataset and the pitfalls of naive analysis\n",
     "    - How to sort Reddit comments from best to worst (not as easy as you think)\n",
     "    \n",
-    "* [**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/Chapter5.ipynb)\n",
+    "* [**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/Ch5_LossFunctions_PyMC3.ipynb)\n",
     "    The introduction of Loss functions and their (awesome) use in Bayesian methods.  Examples include:\n",
     "    - Solving the Price is Right's Showdown\n",
     "    - Optimizing financial predictions\n",
     "    - Winning solution to the Kaggle Dark World's competition.\n",
     "    \n",
-    "* [**Chapter 6: Getting our *prior*-ities straight**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/Chapter6.ipynb)\n",
+    "* [**Chapter 6: Getting our *prior*-ities straight**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/Ch6_Priors_PyMC3.ipynb)\n",
     "    Probably the most important chapter. We draw on expert opinions to answer questions. Examples include:\n",
     "    - Multi-Armed Bandits and the Bayesian Bandit solution.\n",
     "    - what is the relationship between data sample size and prior?\n",
     "    - estimating financial unknowns using expert priors.\n",
     "    \n",
     "    We explore useful tips to be objective in analysis, and common pitfalls of priors. \n",
-    "   \n",
-    "* **Chapter X1: Bayesian Markov Models**\n",
-    "    \n",
-    "* **Chapter X2: Bayesian methods in Machine Learning** \n",
-    "    We explore how to resolve the overfitting problem plus popular ML methods. Also included are probablistic explanations of Ridge Regression and LASSO Regression.\n",
-    "    - Bayesian spam filtering plus *how to defeat Bayesian spam filtering*\n",
-    "    - Tim Saliman's winning solution to Kaggle's *Don't Overfit* problem \n",
-    "    \n",
-    "* **Chapter X3: More PyMC Hackery**\n",
-    "    We explore the gritty details of PyMC. Examples include:\n",
-    "    -  Analysis on real-time GitHub repo stars and forks.\n",
-    "\n",
-    "* **Chapter X4: Troubleshooting and debugging**\n",
-    "\n",
+    " \n",
     "    \n",
     "**More questions about PyMC?**\n",
     "Please post your modeling, convergence, or any other PyMC question on [cross-validated](http://stats.stackexchange.com/), the statistics stack-exchange.\n",
@@ -291,7 +278,7 @@
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