Bayesian Conjugate Gibbs Sampler (BCGS)
Lightweight, pure-Python conjugate sampling
BCGS is an implementation of Markov chain monte carlo using conjugate Gibbs sampling for performing Bayesian inference. Compared to software like JAGS and BUGS, BCGS is extremely crude and limited. It exists mainly for pedagogical purposes. It may also be a convenient solution for simple inference problems, as it is written in pure Python, with no dependences beyond numpy and scipy, and requires no special installation.
Installation
Either pip install bcgs or just download the source bcgs/bcgs.py file from the repository.
Documentation
See the associated github.io page. The notebook from which it is generated can be found here.
1 maintainer
Metadata
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Release files for bcgs 0.2.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| bcgs-0.2.0.tar.gz | 5.8 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| bcgs-0.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 10.6 kB
Release files / bcgs-0.2.0.tar.gz
| Download URL | bcgs-0.2.0.tar.gz |
|---|---|
| Size | 5.8 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/5.1.1 CPython/3.10.8
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Release files / bcgs-0.2.0-py3-none-any.whl
| Download URL | bcgs-0.2.0-py3-none-any.whl |
|---|---|
| Size | 4.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.10.8
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