Factual package intelligence from PyPI
The paper provides a simple and easy method to employ the Bayesian paradigm for typical applications in metrology. The suggested choice for the prior, the sampling methods and the analysis of the resulting posterior is covered in this repository.
pip install simplebayesuncertainty
PyPI declares 0 unique dependency rules for this release. Environment markers are shown when supplied by the project.
This release declares no required runtime dependencies in its PyPI metadata.
simplebayesuncertainty publishes 1 wheel and 1 source archive for version 0.0.1. Wheel platform tags: any.
No version-specific Python classifiers are declared.
PyPI does not currently declare: license. PyDeps marks these fields as unknown instead of guessing values.
PyPI lists 1 release with files. The first dated release is ; 1 release falls within the 365 days preceding the latest dated release. The current release files were uploaded on .