viabel
Efficient, lightweight variational inference and approximation bounds
Science Score: 64.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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✓Academic publication links
Links to: arxiv.org -
✓Committers with academic emails
4 of 6 committers (66.7%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (12.5%) to scientific vocabulary
Keywords
Repository
Efficient, lightweight variational inference and approximation bounds
Basic Info
Statistics
- Stars: 43
- Watchers: 3
- Forks: 15
- Open Issues: 7
- Releases: 0
Topics
Metadata Files
README.md
VIABEL: Variational Inference and Approximation Bounds that are Efficient and Lightweight
VIABEL is a library (still in early development) that provides two types of functionality:
- A lightweight, flexible set of methods for variational inference that is agnostic to how the model is constructed. All that is required is a log density and its gradient.
- Methods for computing bounds on the errors of the mean, standard deviation, and variance estimates produced by a continuous approximation to an (unnormalized) distribution. A canonical application is a variational approximation to a Bayesian posterior distribution.
Documentation
For examples and API documentation, see readthedocs.
Installation
You can install the latest stable version using pip install viabel.
Alternatively, you can clone the repository and use the master branch to
get the most up-to-date version.
Citing VIABEL
If you use this package for diagnostics, please cite:
Validated Variational Inference via Practical Posterior Error Bounds. Jonathan H. Huggins, Mikołaj Kasprzak, Trevor Campbell, Tamara Broderick. In Proc. of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Italy. PMLR: Volume 108, 2020.
The equivalent BibTeX entry is:
@inproceedings{Huggins:2020:VI,
author = {Huggins, Jonathan H and Kasprzak, Miko{\l}aj and Campbell, Trevor and Broderick, Tamara},
title = {{Validated Variational Inference via Practical Posterior Error Bounds}},
booktitle = {Proc. of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS)},
year = {2020}
}
If you use this package for variational inference, please cite:
Robust, Automated, and Accurate Black-box Variational Inference. Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins. arXiv:2203.15945 stat.ML.
The equivalent BibTeX entry is:
@article{Welandawe:2022:BBVI,
author = {Welandawe, Manushi and Andersen, Michael Riis and Vehtari, Aki and Huggins, Jonathan H},
title = {Robust, Automated, and Accurate Black-box Variational Inference},
journal = {arXiv},
volume = {arXiv:2203.15945 [stat.ML]},
year = {2022}
}
Owner
- Name: Jonathan Huggins
- Login: jhuggins
- Kind: user
- Location: Boston, MA
- Company: Boston University
- Website: http://jhhuggins.org
- Repositories: 1
- Profile: https://github.com/jhuggins
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Huggins" given-names: "Jonathan H." orcid: "https://orcid.org/0000-0002-9256-6727" title: "VIABEL: Variational Inference and Approximation Bounds that are Efficient and Lightweight" version: 0.4.2 date-released: 2021-02-06 url: "https://github.com/jhuggins/viabel"
GitHub Events
Total
- Watch event: 3
Last Year
- Watch event: 3
Committers
Last synced: almost 3 years ago
All Time
- Total Commits: 152
- Total Committers: 6
- Avg Commits per committer: 25.333
- Development Distribution Score (DDS): 0.375
Top Committers
| Name | Commits | |
|---|---|---|
| Jonathan Huggins | j****s@m****u | 95 |
| Manushi Welandawe | m****w@b****u | 30 |
| Alejandro Catalina | a****l@g****m | 11 |
| Sihan Liu | s****n@b****u | 9 |
| akash dhaka | a****e@g****m | 6 |
| Manushi Welandawe | m****w@s****u | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: over 1 year ago
All Time
- Total issues: 14
- Total pull requests: 44
- Average time to close issues: 11 days
- Average time to close pull requests: 7 days
- Total issue authors: 2
- Total pull request authors: 7
- Average comments per issue: 0.5
- Average comments per pull request: 0.77
- Merged pull requests: 36
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 6
- Average time to close issues: N/A
- Average time to close pull requests: 4 days
- Issue authors: 0
- Pull request authors: 2
- Average comments per issue: 0
- Average comments per pull request: 0.5
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- jhuggins (13)
- martiningram (1)
Pull Request Authors
- jhuggins (22)
- Manushi22 (8)
- CyrusZhang73 (4)
- cdshrey (3)
- Kwuin (2)
- avehtari (1)
- adhaka (1)
- AlejandroCatalina (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 24 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 7
- Total maintainers: 1
pypi.org: viabel
Efficient, lightweight variational inference and approximation bounds
- Homepage: https://github.com/jhuggins/viabel/
- Documentation: https://viabel.readthedocs.io/
- License: MIT License
-
Latest release: 0.5.1
published over 3 years ago
Rankings
Maintainers (1)
Dependencies
- autoflake *
- codecov *
- coverage *
- flake8 *
- isort *
- pystan ==2.19.1.1
- pytest *
- ipykernel *
- ipython *
- matplotlib *
- nbsphinx *
- nbstripout *
- numpydoc *
- pystan *
- seaborn *
- sphinx >=1.4
- sphinx-copybutton *
- sphinx_rtd_theme *
- autograd *
- numpy >=1.13
- paragami *
- scipy *
- tqdm *