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README.md

Tutorial: Probabilistic Programming using PyMC3

DataScience LA, 24 January, 2017

Binder

Probabilistic Programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models. This class of MCMC, known as Hamiltonian Monte Carlo, requires gradient information which is often not readily available. PyMC3 is a new open source Probabilistic Programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for better performance. Contrary to other Probabilistic Programming languages, PyMC3 allows model specification directly in Python code. This workshop will introduce new users to the PyMC3 package, and demonstrate how to implement and fit models.

Syllabus

Introduction to PyMC3

  • Variable types
  • Probability models
  • Simple case studies

Markov Chain Monte Carlo

  • Metropolis sampling
  • Gradient-based sampling methods

Approximation Methods

  • MAP
  • Variational inference
  • ADVI

Model Building with PyMC3

  • Specifying priors and likelihoods
  • Deterministic variables
  • Factor potentials
  • Custom variables
  • Step methods
  • Generalized linear models
  • Missing Data

Model Checking and Output Processing

  • Storage backends
  • Convergence diagnostics
  • Goodness of fit
  • Plotting and summarization

Software Installation

Running PyMC3 requires a working Python interpreter, either version 2.7 (or more recent) or 3.4 (or more recent); we recommend that new users install version 3.5 (but see special note below if you are a Windows user). A complete Python installation for Mac OSX, Linux and Windows can most easily be obtained by downloading and installing the free Anaconda Python Distribution by ContinuumIO.

PyMC3 can be installed using conda, a package management tool that is bundled with Anaconda. PyMC3 also depends on several third-party Python packages which will be automatically installed when installing via conda. The four required dependencies are: Theano, NumPy, SciPy, Matplotlib, and joblib. To take full advantage of PyMC3, the optional dependencies seaborn, pandas and Patsy should also be installed. You can install PyMC3 and its dependencies by cloning this repository:

git clone https://github.com/fonnesbeck/PyMC3_DataScienceLA.git

Then move into the directory created by the clone, and install the required packages using conda:

cd PyMC3_DataScienceLA
conda env create -f environment.yml

This will create a virtual environment called pymc_tutorial that includes the dependencies for PyMC3 that is completely separate from any other Python installations you may have on your machine. To activate this environment to run the course materials, you can run the following command from the terminal:

source activate pymc_tutorial

If you would rather not install the software yourself, you can use the MyBinder.org link at the top of the page to run the course materials on a remote server

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PyMC3 tutorial for DataScience LA (January 2017)

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