brendanhasz / probflow
A Python package for building Bayesian models with TensorFlow or PyTorch
Why this job: works in the areas we cover, but does not say which job it does
What it does
ProbFlow is a Python package for building probabilistic Bayesian models with TensorFlow 2.0 or PyTorch or JAX, performing stochastic variational inference with those models, and evaluating the models' inferences. It provides both high-level modules for building Bayesian neural networks, as well as low-level parameters and distributions for constructing custom Bayesian models.
Get started
pip install probflow- Maintenance
- Active
- Last commit 47 days ago
- Stars
- 175
- On GitHub
- Licence
- MIT
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v2.7.2
- Released 47 days ago
- Built with
- Jupyter Notebook
- 64% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 55% closed
- 19 still open
- Pull requests
- 36 merged
- 0 waiting
- Releases
- 21
- 7 in the last year
- No contributing guide
- No security policy
- No code of conduct
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More: languages, topics and project record
Languages
- Jupyter Notebook 64%
- Python 36%
- Makefile <1%
Topics
- bayesian
- bayesian-inference
- bayesian-methods
- bayesian-neural-networks
- bayesian-statistics
- data-science
- machine-learning
- python
- pytorch
- statistics
- tensorflow
Project record
- JobOther AI tools
- StatusListed
- Commits710
- Forks18
- Watching4
- Stars in 28 days0
- CreatedJan 5, 2019
- First seen by usNov 7, 2026
- CheckedNov 8, 2026