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Probabilistic Programming and Bayesian Methods for Hackers
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Probabilistic Programming & Bayesian Methods for Hackers
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========
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## *Using Python and PyMC*
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The book can be read in three different ways, starting from most recommended to least recommended:
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1. The most recommended option is to clone the repository and download the .ipynb files to your local machine. If you have IPython installed, you can view the
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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
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chapters in your browser *plus* edit and run the code provided (and try some practice questions). This is the preferred option to read
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this book, though it comes with some dependencies.
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- IPython 0.13 is a requirement to view the ipynb files. It can be downloaded [here](http://ipython.org/ipython-doc/dev/install/index.html)
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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. The most traditional approach, but also not recommended, is to read the chapters as PDFs contained in the `previews` folder. The content
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in these PDFs is not guaranteed to be the most recent content as the PDFs are only compiled periodically. Similarly, the book will not be
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interactive.
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3. **PDF versions are coming.** 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.
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Installation and configuration
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- [Vannessa Sabino](http://baniverso.com/)
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- [Thomas Bratt](https://github.com/thomasbratt)
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- [Nisan Haramati](https://github.com/nisanharamati)
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- [Thomas Bratt](https://github.com/thomasbratt)
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- [Robert Grant](https://github.com/bgrant)
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- [Yaroslav Halchenko](https://github.com/yarikoptic)
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- [Alex Garel](https://github.com/alexgarel)
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We would like to thank the Python community for building an amazing architecture. We would like to thank the

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