A Python probabilistic programming interface to TensorFlow, for Bayesian modelling and machine learning.
High-level interface to TensorFlow Probability. Do not use for anything serious.
- 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
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