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.
There are two different talks on the same topic (EuroPython and PyData):
Depending on what you already installed, you may need to take the following steps:
On OS X, you may need to install MacTex from http://mirror.ctan.org/systems/mac/mactex/MacTeX.pkg
pip install brewer2mpl brew install git pip install git+https://github.com/olgabot/prettyplotlib.git
pip install git+https://github.com/pymc-devs/pymc
pip install patsy pip install statsmodels
pip install zipline