update links and text to refer to Jupyter

Author: ManuelWeiss

README.md

@@ -37,38 +37,38 @@ See the project homepage [here](http://camdavidsonpilon.github.io/Probabilistic-
 
 
 The below chapters are rendered via the *nbviewer* at
-[nbviewer.ipython.org/](http://nbviewer.ipython.org/), and is read-only and rendered in real-time.
+[nbviewer.jupyter.org/](http://nbviewer.jupyter.org/), and is read-only and rendered in real-time.
 Interactive notebooks + examples can be downloaded by cloning! 
 
 ### PyMC2
 
-* [**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.
+* [**Prologue:**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Prologue/Prologue.ipynb) Why we do it.
 
-* [**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_PyMC2.ipynb)
+* [**Chapter 1: Introduction to Bayesian Methods**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Ch1_Introduction_PyMC2.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
 
-* [**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_PyMC2.ipynb)
+* [**Chapter 2: A little more on PyMC**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC2.ipynb)
     We explore modeling Bayesian problems using Python's PyMC library through examples. How do we create Bayesian models? Examples include:
     - Detecting the frequency of cheating students, while avoiding liars
     - Calculating probabilities of the Challenger space-shuttle disaster
 
-* [**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_PyMC2.ipynb)
+* [**Chapter 3: Opening the Black Box of MCMC**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/Ch3_IntroMCMC_PyMC2.ipynb)
     We discuss how MCMC operates and diagnostic tools. Examples include:
     - Bayesian clustering with mixture models
 
-* [**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_PyMC2.ipynb)
+* [**Chapter 4: The Greatest Theorem Never Told**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC2.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
     - How to sort Reddit comments from best to worst (not as easy as you think)
 
-* [**Chapter 5: Would you rather lose 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_PyMC2.ipynb)
+* [**Chapter 5: Would you rather lose an arm or a leg?**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC2.ipynb)
     The introduction of loss functions and their (awesome) use in Bayesian methods.  Examples include:
     - Solving the *Price is Right*'s Showdown
     - Optimizing financial predictions
     - Winning solution to the Kaggle Dark World's competition
 
-* [**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_PyMC2.ipynb)
+* [**Chapter 6: Getting our *prior*-ities straight**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/Ch6_Priors_PyMC2.ipynb)
     Probably the most important chapter. We draw on expert opinions to answer questions. Examples include:
     - Multi-Armed Bandits and the Bayesian Bandit solution.
     - What is the relationship between data sample size and prior?
@@ -78,33 +78,33 @@ Interactive notebooks + examples can be downloaded by cloning!
 
 ### PyMC3
 
-* [**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.
+* [**Prologue:**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Prologue/Prologue.ipynb) Why we do it.
 
-* [**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)
+* [**Chapter 1: Introduction to Bayesian Methods**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Ch1_Introduction_PyMC3.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
 
-* [**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)
+* [**Chapter 2: A little more on PyMC**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC3.ipynb)
     We explore modeling Bayesian problems using Python's PyMC library through examples. How do we create Bayesian models? Examples include:
     - Detecting the frequency of cheating students, while avoiding liars
     - Calculating probabilities of the Challenger space-shuttle disaster
 
-* [**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)
+* [**Chapter 3: Opening the Black Box of MCMC**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb)
     We discuss how MCMC operates and diagnostic tools. Examples include:
     - Bayesian clustering with mixture models
 
-* [**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)
+* [**Chapter 4: The Greatest Theorem Never Told**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC3.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
     - How to sort Reddit comments from best to worst (not as easy as you think)
 
-* [**Chapter 5: Would you rather lose 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)
+* [**Chapter 5: Would you rather lose an arm or a leg?**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC3.ipynb)
     The introduction of loss functions and their (awesome) use in Bayesian methods.  Examples include:
     - Solving the *Price is Right*'s Showdown
     - Optimizing financial predictions
     - Winning solution to the Kaggle Dark World's competition
 
-* [**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)
+* [**Chapter 6: Getting our *prior*-ities straight**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/Ch6_Priors_PyMC3.ipynb)
     Probably the most important chapter. We draw on expert opinions to answer questions. Examples include:
     - Multi-Armed Bandits and the Bayesian Bandit solution.
     - What is the relationship between data sample size and prior?
@@ -124,34 +124,34 @@ Using the book
 
 The book can be read in three different ways, starting from most recommended to least recommended: 
 
-1. The most recommended option is to clone the repository to download the .ipynb files to your local machine. If you have IPython installed, you can view the 
+1. The most recommended option is to clone the repository to download the .ipynb files to your local machine. If you have Jupyter installed, you can view the 
 chapters in your browser *plus* edit and run the code provided (and try some practice questions). This is the preferred option to read
 this book, though it comes with some dependencies. 
-    -  IPython v2.0 (or greater) is a requirement to view the ipynb files. It can be downloaded [here](http://ipython.org/). IPython notebooks can be run by `(your-virtualenv) ~/path/to/the/book/Chapter1_Introduction $ ipython notebook`
+    -  Jupyter is a requirement to view the ipynb files. It can be downloaded [here](http://jupyter.org/). Jupyter notebooks can be run by `(your-virtualenv) ~/path/to/the/book/Chapter1_Introduction $ jupyter notebook`
     -  For Linux users, you should not have a problem installing NumPy, SciPy, Matplotlib and PyMC. For Windows users, check out [pre-compiled versions](http://www.lfd.uci.edu/~gohlke/pythonlibs/) if you have difficulty. 
     -  In the styles/ directory are a number of files (.matplotlirc) that used to make things pretty. These are not only designed for the book, but they offer many improvements over the default settings of matplotlib.
-2. The second, preferred, option is to use the nbviewer.ipython.org site, which display IPython notebooks in the browser ([example](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Chapter1.ipynb)).
+2. The second, preferred, option is to use the nbviewer.jupyter.org site, which display Jupyter notebooks in the browser ([example](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Chapter1.ipynb)).
 The contents are updated synchronously as commits are made to the book. You can use the Contents section above to link to the chapters.
 
-3. PDFs are the least-prefered method to read the book, as pdf's are static and non-interactive. If PDFs are desired, they can be created dynamically using the [nbconvert](https://github.com/ipython/nbconvert) utility.
+3. PDFs are the least-prefered method to read the book, as pdf's are static and non-interactive. If PDFs are desired, they can be created dynamically using the [nbconvert](https://github.com/jupyter/nbconvert) utility.
 
 
 Installation and configuration
 ------
 
 
-If you would like to run the IPython notebooks locally, (option 1. above), you'll need to install the following:
+If you would like to run the Jupyter notebooks locally, (option 1. above), you'll need to install the following:
 
--  IPython 2.0+ is a requirement to view the ipynb files. It can be downloaded [here](http://ipython.org/ipython-doc/dev/install/index.html) 
+-  Jupyter is a requirement to view the ipynb files. It can be downloaded [here](http://jupyter.org/install.html) 
 - Necessary packages are PyMC, NumPy, SciPy and Matplotlib.   
    -  For Linux/OSX users, you should not have a problem installing the above, [*except for Matplotlib on OSX*](http://www.penandpants.com/2012/02/24/install-python/).
    -  For Windows users, check out [pre-compiled versions](http://www.lfd.uci.edu/~gohlke/pythonlibs/) if you have difficulty. 
    - also recommended, for data-mining exercises, are [PRAW](https://github.com/praw-dev/praw) and [requests](https://github.com/kennethreitz/requests). 
-- New to Python or IPython, and help with the namespaces? Check out [this answer](http://stackoverflow.com/questions/12987624/confusion-between-numpy-scipy-matplotlib-and-pylab). 
+- New to Python or Jupyter, and help with the namespaces? Check out [this answer](http://stackoverflow.com/questions/12987624/confusion-between-numpy-scipy-matplotlib-and-pylab). 
 
 -  In the styles/ directory are a number of files that are customized for the notebook. 
 These are not only designed for the book, but they offer many improvements over the 
-default settings of matplotlib and the IPython notebook. The in notebook style has not been finalized yet.
+default settings of matplotlib and the Jupyter notebook. The in notebook style has not been finalized yet.
 
 
 
@@ -171,7 +171,7 @@ feel free to start there.
 -  Giving better explanations
 -  Spelling/grammar mistakes
 -  Suggestions
--  Contributing to the IPython notebook styles
+-  Contributing to the Jupyter notebook styles
 
 
 ####Commiting
@@ -220,8 +220,8 @@ statistics community for building an amazing architecture.
 
 Similarly, the book is only possible because of the [PyMC](http://github.com/pymc-devs/pymc) library. A big thanks to the core devs of PyMC: Chris Fonnesbeck, Anand Patil, David Huard and John Salvatier.
 
-One final thanks. This book was generated by IPython Notebook, a wonderful tool for developing in Python. We thank the IPython 
-community for developing the Notebook interface. All IPython notebook files are available for download on the GitHub repository. 
+One final thanks. This book was generated by Jupyter Notebook, a wonderful tool for developing in Python. We thank the IPython/Jupyter 
+community for developing the Notebook interface. All Jupyter notebook files are available for download on the GitHub repository.