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Merge branch 'cam-master' into chap4-style
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‎Chapter1_Introduction/Chapter1_Introduction.ipynb‎

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"### References\n",
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"\n",
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"- [1] Gelman, Andrew. N.p.. Web. 22 Jan 2013. [N is never large enough](http://andrewgelman.com/2005/07/n_is_never_large).\n",
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"- [1] Gelman, Andrew. N.p.. Web. 22 Jan 2013. [N is never large enough](http://andrewgelman.com/2005/07/31/n_is_never_larg/).\n",
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"- [2] Norvig, Peter. 2009. [The Unreasonable Effectiveness of Data](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35179.pdf).\n",
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"- [3] Patil, A., D. Huard and C.J. Fonnesbeck. 2010. \n",
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"PyMC: Bayesian Stochastic Modelling in Python. Journal of Statistical \n",
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‎Chapter2_MorePyMC/MorePyMC.ipynb‎

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‎Chapter3_MCMC/IntroMCMC.ipynb‎

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‎Chapter4_TheGreatestTheoremNeverTold/LawOfLargeNumbers.ipynb‎

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‎Chapter5_LossFunctions/LossFunctions.ipynb‎

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‎Chapter6_Priorities/Priors.ipynb‎

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‎ExamplesFromChapters/Chapter3/ClusteringWithGaussians.py‎

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assignment = pm.Categorical("assignment", [p, 1-p], size = data.shape[0] )
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taus = 1.0/mc.Uniform( "stds", 0, 100, size= 2)**2 #notice the size!
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taus = 1.0/pm.Uniform( "stds", 0, 100, size= 2)**2 #notice the size!
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"""

‎README.md‎

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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_Introduction.ipynb)).
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The contents are updated synchronously as commits are made to the book. You can use the Contents section above to link to the chapters.
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3. **PDF versions are available! Look in the PDF/ directory.** 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 Chrome's builtin print-to-pdf feature or using the [nbconvert](https://github.com/ipython/nbconvert) utility.
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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.
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Installation and configuration
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- 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/).
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- For Windows users, check out [pre-compiled versions](http://www.lfd.uci.edu/~gohlke/pythonlibs/) if you have difficulty.
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- also recommended, for data-mining exercises, are [PRAW](https://github.com/praw-dev/praw) and [requests](https://github.com/kennethreitz/requests).
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- 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].
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- 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).
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- In the styles/ directory are a number of files that are customized for the notebook.
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These are not only designed for the book, but they offer many improvements over the

‎styles/custom.css‎

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