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Industry

Business & Industry Applications

Language

Python

Features

Modeling
Statistical Computing
Machine Learning

PyMC is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms. PyMC features intuitive model specification syntax, powerful sampling algorithms, variational inference, and transparent support for missing value imputation. It relies on Theano, which provides computation optimization and dynamic C compilation, NumPy broadcasting and advanced indexing, linear algebra operators, and simple extensibility.

PyMC enjoys wide adoption in industry. Quantopian uses PyMC to track uncertainty in the performance of a trading algorithm. We Are Wizards uses PyMC3 to evaluate A/B test performance. VoiceBox Technologies uses PyMC to compare algorithm performances using Kruschke’s BEST algorithm. PyMC is used in research code at Channel 4 for developing internal forecasting tools. Managed by Q uses PyMC for all of their statistical modeling, including A/B test analysis, sales forecasting, and churn prediction. PyMC is used as a primary tool for statistical modeling at Salesforce, where they use it to build hierarchical models to evaluate varying effects in web experiments and then to build meta-analyses that quantify the expected returns of a subsequent experiment. It has also been used at Monetate, GrubHub, and DataXu.

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