Probabilistic Programming allows flexible specification of statistical models to gain insight from data. Estimation of best fitting parameter values, as well as uncertainty in these estimations, can be automated by sampling algorithms like Markov chain Monte Carlo (MCMC). The high interpretability and flexibility of this approach has lead to a huge paradigm shift in scientific fields ranging from Cognitive Science to Data Science and Quantitative Finance.
PyMC3 is a new Python module that features next generation sampling algorithms and an intuitive model specification syntax. The whole code base is written in pure Python and Just-in-time compiled via Theano for speed.
In this talk I will provide an intuitive introduction to Bayesian statistics and how probabilistic models can be specified and estimated using PyMC3.
- Video of the talk at EuroPython
- The reveal slide show EuroPython version
- Video of the talk at PyData
- The reveal slide show PyData version
- PyMC repo
There are two different talks on the same topic (EuroPython and PyData):
- EuroPython 2014 conference Berlin on Jul 24 2014 (different material)
- PyData conference NYC on Nov 9 2013
- Data Mining Meet-up in Boston on Oct 15 2013
Depending on what you already installed, you may need to take the following steps:
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On OS X, you may need to install MacTex from http://mirror.ctan.org/systems/mac/mactex/MacTeX.pkg
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pip install brewer2mpl brew install git pip install git+https://github.com/olgabot/prettyplotlib.git
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pip install git+https://github.com/pymc-devs/pymc
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pip install patsy pip install statsmodels
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pip install zipline