### 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

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