Friendly modelling API
PyMC3 allows you to write down models using an intuitive syntax to describe a data generating process.
Cutting edge algorithms and model building blocks
Fit your model using gradient-based MCMC algorithms like NUTS, using ADVI for fast approximate inference — including minibatch-ADVI for scaling to large datasets — or using Gaussian processes to build Bayesian nonparametric models.
import pymc3 as pm
X, y = linear_training_data()
with pm.Model() as linear_model:
weights = pm.Normal('weights', mu=0, sd=1)
noise = pm.Gamma('noise', alpha=2, beta=1)
y_observed = pm.Normal('y_observed',
mu=X.dot(weights),
sd=noise,
observed=y)
prior = pm.sample_prior_predictive()
posterior = pm.sample()
posterior_pred = pm.sample_posterior_predictive(posterior)
Installation
Via conda-forge:
conda install -c conda-forge pymc3
Via pypi:
pip install pymc3
Latest (unstable):
pip install git+https://github.com/pymc-devs/pymc3
In-Depth Guides
License
PyMC3 is licensed under the Apache License, V2.
Citing PyMC3
Salvatier J., Wiecki T.V., Fonnesbeck C. (2016) Probabilistic programming in Python using PyMC3. PeerJ Computer Science 2:e55 DOI: 10.7717/peerj-cs.55.
See Google Scholar for a continuously updated list of papers citing PyMC3.
Support and sponsors
PyMC3 is a non-profit project under NumFOCUS umbrella. If you want to support PyMC3 financially, you can donate here.