https://github.com/matteobreschi/bajes
Bayesian Jenaer software
https://github.com/matteobreschi/bajes
astrophysics bayesian-inference gravitational-waves kilonovae multi-messenger python sampling-methods
Last synced: 19 days ago
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Bayesian Jenaer software
- Host: GitHub
- URL: https://github.com/matteobreschi/bajes
- Owner: matteobreschi
- License: mit
- Created: 2021-01-30T08:40:15.000Z (about 5 years ago)
- Default Branch: release/v1.2.0
- Last Pushed: 2025-09-08T14:46:40.000Z (8 months ago)
- Last Synced: 2026-01-02T21:13:33.009Z (4 months ago)
- Topics: astrophysics, bayesian-inference, gravitational-waves, kilonovae, multi-messenger, python, sampling-methods
- Language: Python
- Homepage:
- Size: 59.4 MB
- Stars: 22
- Watchers: 5
- Forks: 14
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://pypi.org/project/bajes/)
[](https://pypi.org/project/bajes/)
[](https://pypi.org/project/bajes/)
*bajes* [baɪɛs] is a Python software for Bayesian inference developed at Friedrich-Schiller-Universtät Jena
and specialized in the analysis of gravitational-wave and multi-messenger transients.
The software is designed to be state-of-art, simple-to-use and light-weighted
with minimal dependencies on external libraries.
## Installation
*bajes* is compatible with Python v3.7 (or higher)
and it is built on modules that can be easily installed via `pip`.
The mandatory dependencies are `numpy`, `scipy` and `astropy`.
However, the user might need to download some further packages.
See [`INSTALL`](https://github.com/matteobreschi/bajes/tree/release/v1.2.0/INSTALL.md) for more information.
## Modules
*bajes* provides an homonymous Python module that includes:
* `bajes.inf`: implementation of the statistical objects and Bayesian workflow,
* `bajes.obs`: tools and methods for data analysis of multi-messenger signals.
For more details, visit [`gw_tutorial`](https://github.com/matteobreschi/bajes/tree/release/v1.2.0/docs/gw_tutorial.ipynb).
## Inference
The *bajes* package provides a user-friendly interface capable to easily set up a
Bayesian analysis for an arbitrary model. Providing a prior file and a likelihood function, the command
python -m bajes -p prior.ini -l like.py -o /path/to/outdir/
will run a parameter estimation job, inferring the properties of the input model.
For more details, visit [`inf_tutorial`](https://github.com/matteobreschi/bajes/tree/release/v1.2.0/docs/inf_tutorial.ipynb)
or type `python -m bajes --help`.
## Pipeline
The *bajes* infrastructure allows the user to set up a pipeline for parameters
estimation of multi-messenger transients.
This can be easily done writing a configuration file,
that contains the information to be passed to the executables.
Subsequently, the following command,
bajes_pipe config.ini
will generates the requested output directory, if it does not exists, and
the pipeline will be written into a bash executable (`/path/to/outdir/jobname.sub`).
For more details, visit [`conifg_example`](https://github.com/matteobreschi/bajes/tree/release/v1.2.0/docs/config_example.ini).
The GW pipeline incorporates an interface with reduced-order-quadratude (ROQ) interpolants.
In particular, the ROQ pipeline relies on the output provided by [`JenpyROQ`](https://github.com/gcarullo/JenpyROQ).
## Credits
*bajes* is developed at the Friedrich-Schiller-Universität Jena,
visit [`CREDITS`](https://github.com/matteobreschi/bajes/tree/release/v1.2.0/CREDITS.md) for more details.
If you find *bajes* useful in your research, please include the following [citation](https://arxiv.org/abs/2102.00017) in your publication,
@article{Bajes:2021,
author = "Breschi, Matteo and Gamba, Rossella and Bernuzzi, Sebastiano",
title = "{Bayesian inference of multimessenger astrophysical data: Methods and applications to gravitational waves}",
eprint = "2102.00017",
archivePrefix = "arXiv",
primaryClass = "gr-qc",
doi = "10.1103/PhysRevD.104.042001",
journal = "Phys. Rev. D",
volume = "104",
number = "4",
pages = "042001",
year = "2021"
}
## Acknowledgement
*bajes* has benefited from open source libraries, including the samplers,
* [`cpnest`](https://johnveitch.github.io/cpnest/)
* [`dynesty`](https://dynesty.readthedocs.io/)
* [`emcee`](https://emcee.readthedocs.io/)
* [`ultranest`](https://johannesbuchner.github.io/UltraNest/)
and the gravitational-wave analysis packages,
* [`bilby`](https://lscsoft.docs.ligo.org/bilby/)
* [`gwbinning`](https://bitbucket.org/dailiang8/gwbinning/)
* [`lalsuite`](https://lscsoft.docs.ligo.org/lalsuite/)
* [`pycbc`](https://pycbc.org)
We also acknowledge the LIGO-Virgo-KAGRA Collaboration for maitaining the [GWOSC](https://www.gw-openscience.org).