Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano
Project description
PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.
Check out the getting started guide, or interact with live examples using Binder!
Features
Intuitive model specification syntax, for example, x ~ N(0,1) translates to x = Normal('x',0,1)
Powerful sampling algorithms, such as the No U-Turn Sampler, allow complex models with thousands of parameters with little specialized knowledge of fitting algorithms.
Variational inference: ADVI for fast approximate posterior estimation as well as mini-batch ADVI for large data sets.
- Relies on Theano which provides:
Computation optimization and dynamic C compilation
Numpy broadcasting and advanced indexing
Linear algebra operators
Simple extensibility
Transparent support for missing value imputation
Getting started
If you already know about Bayesian statistics:
Learn Bayesian statistics with a book together with PyMC3:
Probabilistic Programming and Bayesian Methods for Hackers: Fantastic book with many applied code examples.
PyMC3 port of the book “Doing Bayesian Data Analysis” by John Kruschke as well as the second edition: Principled introduction to Bayesian data analysis.
PyMC3 port of the book “Bayesian Cognitive Modeling” by Michael Lee and EJ Wagenmakers: Focused on using Bayesian statistics in cognitive modeling.
Bayesian Analysis with Python (second edition) by Osvaldo Martin: Great introductory book. (code and errata).
PyMC3 talks
There are also several talks on PyMC3 which are gathered in this YouTube playlist
Installation
The latest release of PyMC3 can be installed from PyPI using pip:
pip install pymc3
Note: Running pip install pymc will install PyMC 2.3, not PyMC3, from PyPI.
Or via conda-forge:
conda install -c conda-forge pymc3
Plotting is done using ArviZ which may be installed separately, or along with PyMC3:
pip install pymc3[plots]
The current development branch of PyMC3 can be installed from GitHub, also using pip:
pip install git+https://github.com/pymc-devs/pymc3
To ensure the development branch of Theano is installed alongside PyMC3 (recommended), you can install PyMC3 using the requirements.txt file. This requires cloning the repository to your computer:
git clone https://github.com/pymc-devs/pymc3 cd pymc3 pip install -r requirements.txt
However, if a recent version of Theano has already been installed on your system, you can install PyMC3 directly from GitHub.
Another option is to clone the repository and install PyMC3 using python setup.py install or python setup.py develop.
Dependencies
PyMC3 is tested on Python 3.6 and depends on Theano, NumPy, SciPy, and Pandas (see requirements.txt for version information).
Optional
In addtion to the above dependencies, the GLM submodule relies on Patsy.
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.
Contact
We are using discourse.pymc.io as our main communication channel. You can also follow us on Twitter @pymc_devs for updates and other announcements.
To ask a question regarding modeling or usage of PyMC3 we encourage posting to our Discourse forum under the “Questions” Category. You can also suggest feature in the “Development” Category.
To report an issue with PyMC3 please use the issue tracker.
Finally, if you need to get in touch for non-technical information about the project, send us an e-mail.
License
Software using PyMC3
Exoplanet: a toolkit for modeling of transit and/or radial velocity observations of exoplanets and other astronomical time series.
Bambi: BAyesian Model-Building Interface (BAMBI) in Python.
pymc3_models: Custom PyMC3 models built on top of the scikit-learn API.
PMProphet: PyMC3 port of Facebook’s Prophet model for timeseries modeling
webmc3: A web interface for exploring PyMC3 traces
sampled: Decorator for PyMC3 models.
NiPyMC: Bayesian mixed-effects modeling of fMRI data in Python.
beat: Bayesian Earthquake Analysis Tool.
Please contact us if your software is not listed here.
Papers citing PyMC3
See Google Scholar for a continuously updated list.
Contributors
See the GitHub contributor page
Support
PyMC3 is a non-profit project under NumFOCUS umbrella. If you want to support PyMC3 financially, you can donate here.
Sponsors
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file pymc3-3.9.1.tar.gz.
File metadata
- Download URL: pymc3-3.9.1.tar.gz
- Upload date:
- Size: 1.8 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.7.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 2b59707679f9a05494a98544d52f507bc9ce2506ce01acd692075aa89e1023a9 |
|
| MD5 | 7afc8a1987e8fee82f7cb8df3a9602ac |
|
| BLAKE2b-256 | b19e43311b9956ec4f9a70d38988a8ae3c9d2af2e9fee51dd23c59387fa3a68c |
Provenance
File details
Details for the file pymc3-3.9.1-py3-none-any.whl.
File metadata
- Download URL: pymc3-3.9.1-py3-none-any.whl
- Upload date:
- Size: 1.9 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.7.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 0391482d42f9a29217b446e3d9f093d34b2cc2569fb395e21348f29180437a91 |
|
| MD5 | 39e6069cb43cb74e5100b250f9c8184e |
|
| BLAKE2b-256 | d7b4efc2acd1d20655c6cce7d3048b6be75208e91c3af5ace8f8052f82467b0d |
