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Tutorials Examples Books + Videos API Developer Guide About PyMC3



PROBABILISTIC PROGRAMMING IN PYTHON

Quickstart


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, sigma=1)
    noise = pm.Gamma("noise", alpha=2, beta=1)
    y_observed = pm.Normal(
        "y_observed",
        mu=X @ weights,
        sigma=noise,
        observed=y,
    )

    prior = pm.sample_prior_predictive()
    posterior = pm.sample()
    posterior_pred = pm.sample_posterior_predictive(posterior)



INSTALLATION

Instructions for Linux
Instructions for MacOS
Instructions for Windows


IN-DEPTH GUIDES

Probability Distributions
PyMC3 includes a comprehensive set of pre-defined statistical distributions that
can be used as model building blocks.
Gaussian Processes
Sometimes an unknown parameter or variable in a model is not a scalar value or a
fixed-length vector, but a function. A Gaussian process (GP) can be used as a
prior probability distribution whose support is over the space of continuous
functions. PyMC3 provides rich support for defining and using GPs.
Variational Inference
Variational inference saves computational cost by turning a problem of
integration into one of optimization. PyMC3's variational API supports a number
of cutting edge algorithms, as well as minibatch for scaling to large datasets.
PyMC3 and Theano
Theano is the deep-learning library PyMC3 uses to construct probability
distributions and then access the gradient in order to implement cutting edge
inference algorithms. More advanced models may be built by understanding this
layer.


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 value PyMC and
want to support its development, consider donating to the project or read our
support PyMC3 page.


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