<commit_before># PyMC3
[![Gitter](https://badges.gitter.im/Join Chat.svg)](https://gitter.im/pymc-devs/pymc?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

[![Build Status](https://travis-ci.org/pymc-devs/pymc3.png?branch=master)](https://travis-ci.org/pymc-devs/pymc3)

PyMC3 is a python module for Bayesian statistical modeling and model fitting which focuses on advanced Markov chain Monte Carlo fitting algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.

Check out the [Tutorial](http://pymc-devs.github.io/pymc3/getting_started/)!

PyMC3 is Beta software. Users should consider using [PyMC 2 repository](https://github.com/pymc-devs/pymc).

## Features

 * Intuitive model specification syntax, for example, `x ~ N(0,1)` translates to `x = Normal(0,1)`
 * Powerful sampling algorithms such as the [No U-Turn Sampler] allow complex models with thousands of parameters with little specialized knowledge of fitting algorithms.
 * Easy optimization for finding the *maximum a posteriori*(MAP) point
 * [Theano](http://deeplearning.net/software/theano/) features
  * Numpy broadcasting and advanced indexing
  * Linear algebra operators
  * Computation optimization and dynamic C compilation
 * Simple extensibility
 * Transparent support for missing value imputation

## Getting started
 * [PyMC3 Tutorial](http://pymc-devs.github.io/pymc3/getting_started/)
 * Coal Mining Disasters model in [PyMC 2](https://github.com/pymc-devs/pymc/blob/master/pymc/examples/disaster_model.py) and [PyMC 3](https://github.com/pymc-devs/pymc3/blob/master/pymc3/examples/disaster_model.py)
 * [Global Health Metrics & Evaluation model](http://nbviewer.ipython.org/urls/raw.github.com/pymc-devs/pymc3/master/pymc3/examples/GHME%202013.ipynb) case study for GHME 2013
 * [Stochastic Volatility model](http://nbviewer.ipython.org/urls/raw.github.com/pymc-devs/pymc3/master/pymc3/examples/stochastic_volatility.ipynb)
 * [Several blog posts on linear regression](http://twiecki.github.io/tag/bayesian-statistics.html)
 * [Talk at PyData NYC 2013 on PyMC3](http://twiecki.github.io/blog/2013/12/12/bayesian-data-analysis-pymc3/)
 * [PyMC3 port of the models presented in the book "Doing Bayesian Data Analysis" by <NAME>](https://github.com/aloctavodia/Doing_bayesian_data_analysis)
 * [The PyMC3 examples folder](https://github.com/pymc-devs/pymc3/tree/master/pymc3/examples)
<commit_msg>add link for NUTS<commit_after># PyMC3
[![Gitter](https://badges.gitter.im/Join Chat.svg)](https://gitter.im/pymc-devs/pymc?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

[![Build Status](https://travis-ci.org/pymc-devs/pymc3.png?branch=master)](https://travis-ci.org/pymc-devs/pymc3)

PyMC3 is a python module for Bayesian statistical modeling and model fitting which focuses on advanced Markov chain Monte Carlo fitting algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.

Check out the [Tutorial](http://pymc-devs.github.io/pymc3/getting_started/)!

PyMC3 is Beta software. Users should consider using [PyMC 2 repository](https://github.com/pymc-devs/pymc).

## Features

 * Intuitive model specification syntax, for example, `x ~ N(0,1)` translates to `x = Normal(0,1)`
 * Powerful sampling algorithms such as the [No U-Turn Sampler](http://arxiv.org/abs/1111.4246) allow complex models with thousands of parameters with little specialized knowledge of fitting algorithms.
 * Easy optimization for finding the *maximum a posteriori*(MAP) point
 * [Theano](http://deeplearning.net/software/theano/) features
  * Numpy broadcasting and advanced indexing
  * Linear algebra operators
  * Computation optimization and dynamic C compilation
 * Simple extensibility
 * Transparent support for missing value imputation

## Getting started
 * [PyMC3 Tutorial](http://pymc-devs.github.io/pymc3/getting_started/)
 * Coal Mining Disasters model in [PyMC 2](https://github.com/pymc-devs/pymc/blob/master/pymc/examples/disaster_model.py) and [PyMC 3](https://github.com/pymc-devs/pymc3/blob/master/pymc3/examples/disaster_model.py)
 * [Global Health Metrics & Evaluation model](http://nbviewer.ipython.org/urls/raw.github.com/pymc-devs/pymc3/master/pymc3/examples/GHME%202013.ipynb) case study for GHME 2013
 * [Stochastic Volatility model](http://nbviewer.ipython.org/urls/raw.github.com/pymc-devs/pymc3/master/pymc3/examples/stochastic_volatility.ipynb)
 * [Several blog posts on linear regression](http://twiecki.github.io/tag/bayesian-statistics.html)
 * [Talk at PyData NYC 2013 on PyMC3](http://twiecki.github.io/blog/2013/12/12/bayesian-data-analysis-pymc3/)
 * [PyMC3 port of the models presented in the book "Doing Bayesian Data Analysis" by <NAME>](https://github.com/aloctavodia/Doing_bayesian_data_analysis)
 * [The PyMC3 examples folder](https://github.com/pymc-devs/pymc3/tree/master/pymc3/examples)

{'fraction_non_alphanumeric': 0.12301748678324523, 'fraction_numerical': 0.021553477023180154, 'mean_word_length': 5.274234693877551, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 18, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '5574749', 'n_tokens_mistral': 1772, 'n_tokens_neox': 1648, 'n_words': 371}