Skip to main content

A Python probabilistic programming interface to TensorFlow, for Bayesian modelling and machine learning.

Project description

PyMC4 (Pre-release)

Build Status Coverage Status

High-level interface to TensorFlow Probability. Do not use for anything serious.

What works?

  • Build most models you could build with PyMC3
  • Sample using NUTS, all in TF, fully vectorized across chains (multiple chains basically become free)
  • Automatic transforms of model to the real line
  • Prior and posterior predictive sampling
  • Deterministic variables
  • Trace that can be passed to ArviZ

However, expect things to break or change without warning.

See here for an example: https://github.com/pymc-devs/pymc4/blob/master/notebooks/radon_hierarchical.ipynb See here for the design document: https://github.com/pymc-devs/pymc4/blob/master/notebooks/pymc4_design_guide.ipynb

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for pymc4, version 4.0a2
Filename, size File type Python version Upload date Hashes
Filename, size pymc4-4.0a2-py3-none-any.whl (85.1 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size pymc4-4.0a2.tar.gz (77.7 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page