A Python probabilistic programming interface to TensorFlow, for Bayesian modelling and machine learning.
Project description
PyMC4 (Pre-release)
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
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.
| 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 |
