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Python Sprint: PyMC3 beginner friendly

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Zopa, Cottons Centre, Tooley St, SE1 2QG, London

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Please RSVP in: https://www.meetup.com/Python-Sprints/events/244062594/

Probabilistic programming are a family of programming languages where a probabilistic model can be specified, in order to do inference over unknown variables.

A common application is in financial markets, where probabilistic programming can be used to infer expected returns or risk.

The best introduction to Bayesian methods and probabilistic programming I know is this excellent book: https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms.

In this sprint we'll address PyMC3 beginner friendly issues.

If the meetup is full, or you can't attend in person, feel free to join remotely:

Gitter channel (chat) during the sprint: https://gitter.im/py-sprints/pymc3

Short videocall at 7pm: Hangouts link

Thanks to our sponsor Zopa, for making this sprint possible.

Please set up a development environment before the sprint:

Fork PyMC3 repository by clicking in the top right button at:

https://github.com/pymc-devs/pymc3

After it completes, run in your computer terminal.

$ git clone https://github.com//pymc3

$ cd pymc3

$ git remote add upstream git@github.com:pymc-devs/pymc3.git

Download and install Anaconda from: https://www.anaconda.com/download/

After restarting the terminal, run:

$ conda config --add channels conda-forge

$ conda create -n pymc3_dev --file /requirements-dev.txt

$ source activate pymc3_dev

Full notes on contributing to PyMC3 can be found here: https://github.com/pymc-devs/pymc3/blob/master/CONTRIBUTING.md

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