BayesianEstimationTool (BET)
BayesianEstimationTool fits population-level parameters and individual-specific latent variables with NumPyro inference methods.
Documentation
Start here:
- Manual (Markdown) — model contracts, likelihoods, masking, JAX modeling, inference, persistence, and testing.
- Manual (PDF) — printable manual. If GitHub cannot preview the PDF, download it to view locally.
- Cheatsheet (Markdown) — compact API reference and common workflows.
- Cheatsheet (PDF) — one-page printable reference. If GitHub cannot preview the PDF, download it to view locally.
Installation
BET requires Python 3.10 or newer. Install from PyPI:
python -m pip install BayesianEstimationTool
Bundled examples and documentation
After installing BET, copy its bundled runnable examples and Markdown and PDF manual/cheatsheet to an editable folder in the current directory (no GitHub download needed):
python -m BayesianEstimationTool.ExampleInstaller
To select a different parent directory:
python -m BayesianEstimationTool.ExampleInstaller ~/Documents
This creates BET-Examples/ with the six Python examples and a Documentation/ subfolder containing Manual.md, Manual.pdf, Cheatsheet.md, and Cheatsheet.pdf. Run a script with python BET-Examples/1_SimpleExampleNuts.py. The installer refuses to overwrite an existing BET-Examples/ directory. The installer is available in package builds that include bundled examples and documentation.
Alternative installation methods
Alternatively, install the latest source directly from GitHub:
python -m pip install "git+https://github.com/rafaelrbravo/BayesianEstimationTool.git"
You can also download a wheel (.whl) or source archive (.tar.gz) from GitHub Releases and install it with python -m pip install PATH_TO_DOWNLOADED_FILE.
Verify installation:
python -c "import BayesianEstimationTool as bet; print(bet.Estimator)"
For editable development from a source checkout, run python -m pip install -e . in the repository root. The conda recipe in conda-recipe/meta.yaml is for local builds; BET is not yet published on conda-forge.
See the manual for the minimal model and training examples.
How to cite
If you use BET in your research, please cite:
Bravo, R., Tercan, B., Lima, E., Aguilar, B., & Yankeelov, T. (2026). BayesianEstimationTool [Computer software]. https://doi.org/10.5281/zenodo.23431103
This DOI identifies all versions of BET. GitHub also provides a Cite this repository option using CITATION.cff.
1 maintainer
Metadata
1 maintainer
Release files for BayesianEstimationTool 1.0.4
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|---|---|---|---|---|
| bayesianestimationtool-1.0.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 817.4 kB
Release files / bayesianestimationtool-1.0.4.tar.gz
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