Bayesian-priors is a package for visualizing prior distributions in the context of bayesian inference. The following continuous distributions are supported: normal, student-t, exponential, gamma, inverse gamma, weibull, pareto, gumbel, log-normal, cauchy, beta. In the dashboard, user inputs their desired lower and upper bounds, along with the % mass in-between. The dashboard will then display a set of parameters that generates such distribution.
1 maintainer
Metadata
1 maintainer
Release files for bayesian-priors 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| bayesian_priors-0.0.1.tar.gz | 15.5 kB | Without provenance | Details for bayesian_priors-0.0.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| bayesian_priors-0.0.1-py3-none-any.whl | Python 3 | none | any | Details for bayesian_priors-0.0.1-py3-none-any.whl |
Total release size: 41.2 kB
Release files / bayesian_priors-0.0.1.tar.gz
| Download URL | bayesian_priors-0.0.1.tar.gz |
|---|---|
| Size | 15.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
266db9430d7aa8e34e39c9cbe702283240572c6104b94d424a6de36338e160c2
|
|
BLAKE2b-256 checksum How to use checksums |
bcccf475e67be62035c59f0fa1459dd16f97202fdc07e4bf879be35a868f23c8
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.8.0 pkginfo/1.8.2 readme-renderer/32.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.62.3 importlib-metadata/4.11.0 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.8.10
|
Release files / bayesian_priors-0.0.1-py3-none-any.whl
| Download URL | bayesian_priors-0.0.1-py3-none-any.whl |
|---|---|
| Size | 25.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
0c09915a26d71eed40bd1322786f301d0a4fb60a5051d3145a917e5c8de25741
|
|
BLAKE2b-256 checksum How to use checksums |
1cdbd6ef45727b38f24b6b27aa8376c3c3dd5f0389e9e2a32680cf162784352e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.8.0 pkginfo/1.8.2 readme-renderer/32.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.62.3 importlib-metadata/4.11.0 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.8.10
|
Security for bayesian-priors 0.0.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
