Package: pymc3-ext-wlad
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 thegetting started guide,  orinteract with live examplesusing Binder!FeaturesIntuitive model specification syntax, for example,x ~ N(0,1)translates tox =Normal('x',0,1)Powerful sampling algorithms, such as theNo U-Turn
Sampler, allow complex models
with thousands of parameters with little specialized knowledge of
fitting algorithms.Variational inference:ADVIfor fast approximate posterior estimation as well as mini-batch ADVI
for large data sets.Relies onTheanowhich provides:Computation optimization and dynamic C compilationNumpy broadcasting and advanced indexingLinear algebra operatorsSimple extensibilityTransparent support for missing value imputationGetting startedIf you already know about Bayesian statistics:API quickstart guideThePyMC3 tutorialPyMC3 examplesand theAPI referenceLearn 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 Kruschkeas well as thesecond edition: Principled introduction to Bayesian data analysis.PyMC3 port of the book “Statistical Rethinking A Bayesian Course with Examples in R and Stan” by Richard McElreathPyMC3 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. (codeand errata).PyMC3 talksThere are also several talks on PyMC3 which are gathered in thisYouTube playlistInstallationThe latest release of PyMC3 can be installed from PyPI usingpip:pip install pymc3Note:Runningpip install pymcwill install PyMC 2.3, not PyMC3,
from PyPI.Or via conda-forge:conda install -c conda-forge pymc3Plotting is done usingArviZwhich may be installed separately, or along with PyMC3:pip install pymc3[plots]The current development branch of PyMC3 can be installed from GitHub, also usingpip:pip install git+https://github.com/pymc-devs/pymc3To ensure the development branch of Theano is installed alongside PyMC3
(recommended), you can install PyMC3 using therequirements.txtfile. This requires cloning the repository to your computer:git clone https://github.com/pymc-devs/pymc3
cd pymc3
pip install -r requirements.txtHowever, 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 usingpython setup.py installorpython setup.py develop.DependenciesPyMC3 is tested on Python 3.6 and depends on Theano, NumPy,
SciPy, and Pandas (seerequirements.txtfor version
information).OptionalIn addtion to the above dependencies, the GLM submodule relies onPatsy.Citing PyMC3Salvatier J., Wiecki T.V., Fonnesbeck C. (2016) Probabilistic programming
in Python using PyMC3. PeerJ Computer Science 2:e55DOI: 10.7717/peerj-cs.55.ContactWe are usingdiscourse.pymc.ioas our main communication channel. You can also follow us onTwitter @pymc_devsfor 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 theissue tracker.Finally, if you need to get in touch for non-technical information about the project,send us an e-mail.LicenseApache License, Version
2.0Software using PyMC3Exoplanet: 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 modelingwebmc3: A web interface for exploring PyMC3 tracessampled: 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 PyMC3SeeGoogle Scholarfor a continuously updated list.ContributorsSee theGitHub contributor
pageSupportPyMC3 is a non-profit project under NumFOCUS umbrella. If you want to support PyMC3 financially, you can donatehere.Sponsors