SBIAX
SBIAX: Density-estimation simulation-based inference in JAX - Published in JOSS (2025)
Science Score: 93.0%
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Published in Journal of Open Source Software
Keywords
Scientific Fields
Repository
Fast, lightweight and parallelised simulation-based inference in JAX.
Basic Info
Statistics
- Stars: 19
- Watchers: 1
- Forks: 1
- Open Issues: 0
- Releases: 21
Topics
Metadata Files
README.md
sbiax
Fast, lightweight and parallel simulation-based inference.
sbiax is a lightweight library for simulation-based inference (SBI) with a fixed grid of simulations.
[!WARNING] :buildingconstruction: Note this repository is under construction, expect changes. :buildingconstruction:
Design
In a typical inference problem the data likelihood is unknown. Using density-estimation SBI, we can proceed by
- simulating a set of data and model parameters ${(\boldsymbol{\xi}, \boldsymbol{\pi})0, ..., (\boldsymbol{\xi}, \boldsymbol{\pi})N}$,
- obtaining a measurement $\hat{\boldsymbol{\xi}}$,
- compressing the simulations and the measurements - usually with a neural network or linear compression - to a set of summaries ${(\boldsymbol{x}, \boldsymbol{\pi})0, ..., (\boldsymbol{x}, \boldsymbol{\pi})N}$ and $\hat{\boldsymbol{x}}$,
- fitting an ensemble of normalising flow or similar density estimation algorithms (e.g. a Gaussian mixture model),
- the optional optimisation of the parameters for the architecture and fitting hyperparameters of the algorithms,
- sampling the ensemble posterior (using an MCMC sampler if the likelihood is fit directly) conditioned on the datavector to obtain parameter constraints on the parameters of a physical model, $\boldsymbol{\pi}$.
sbiax is a code for implementing each of these steps.
Usage
Install via
pip install sbiax
and have a look at examples.
Contributing
Want to add something? See CONTRIBUTING.md.
Citation
If you found this library to be useful in academic work, please cite: <!--(arXiv link) -->
bibtex
@misc{homer2024simulationbasedinferencedodelsonschneidereffect,
title={Simulation-based inference has its own Dodelson-Schneider effect (but it knows that it does)},
author={Jed Homer and Oliver Friedrich and Daniel Gruen},
year={2024},
eprint={2412.02311},
archivePrefix={arXiv},
primaryClass={astro-ph.CO},
url={https://arxiv.org/abs/2412.02311},
}
bibtex
@article{
Homer2025,
doi = {10.21105/joss.07606},
url = {https://doi.org/10.21105/joss.07606},
year = {2025},
publisher = {The Open Journal},
volume = {10},
number = {105},
pages = {7606},
author = {Jed Homer and Oliver Friedrich},
title = {SBIAX: Density-estimation simulation-based inference in JAX},
journal = {Journal of Open Source Software}
}
Owner
- Name: Jed Homer
- Login: homerjed
- Kind: user
- Location: München, Deutschland
- Website: homerjed.github.io
- Repositories: 17
- Profile: https://github.com/homerjed
JOSS Publication
SBIAX: Density-estimation simulation-based inference in JAX
Authors
University Observatory, Faculty for Physics, Ludwig-Maximilians-Universität München, Scheinerstrasse 1, München, Deustchland., Munich Center for Machine Learning.
University Observatory, Faculty for Physics, Ludwig-Maximilians-Universität München, Scheinerstrasse 1, München, Deustchland., Munich Center for Machine Learning., Excellence Cluster ORIGINS, Boltzmannstr. 2, 85748 Garching, Deutschland.
Editor
Mehmet Hakan Satman
Tags
Machine learning Generative models Bayesian Inference Simulation based inferenceGitHub Events
Total
- Create event: 50
- Issues event: 16
- Release event: 19
- Watch event: 18
- Delete event: 1
- Issue comment event: 23
- Push event: 164
- Public event: 1
- Pull request event: 15
- Fork event: 2
Last Year
- Create event: 50
- Issues event: 16
- Release event: 19
- Watch event: 18
- Delete event: 1
- Issue comment event: 23
- Push event: 164
- Public event: 1
- Pull request event: 15
- Fork event: 2
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| homerjed | j****r@g****m | 146 |
| Mehmet Hakan Satman | m****n@g****m | 5 |
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 9
- Total pull requests: 15
- Average time to close issues: 2 days
- Average time to close pull requests: about 13 hours
- Total issue authors: 2
- Total pull request authors: 3
- Average comments per issue: 2.0
- Average comments per pull request: 0.2
- Merged pull requests: 14
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 9
- Pull requests: 15
- Average time to close issues: 2 days
- Average time to close pull requests: about 13 hours
- Issue authors: 2
- Pull request authors: 3
- Average comments per issue: 2.0
- Average comments per pull request: 0.2
- Merged pull requests: 14
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- kazewong (5)
- maja-jablonska (4)
Pull Request Authors
- jbytecode (8)
- maja-jablonska (5)
- homerjed (2)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 87 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 23
- Total maintainers: 1
pypi.org: sbiax
Fast, parallel and lightweight simulation-based inference in JAX.
- Documentation: https://sbiax.readthedocs.io/
- License: MIT License Copyright (c) [2024] [Jed Homer] Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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Latest release: 0.0.57
published 9 months ago
Rankings
Maintainers (1)
Dependencies
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