https://github.com/mj-will/nessai
nessai: Nested Sampling with Artificial Intelligence
https://github.com/mj-will/nessai
bayesian-inference bilby machine-learning nested-sampling normalizing-flows python pytorch
Last synced: 5 months ago
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nessai: Nested Sampling with Artificial Intelligence
- Host: GitHub
- URL: https://github.com/mj-will/nessai
- Owner: mj-will
- License: mit
- Created: 2020-10-23T16:43:14.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2024-04-12T15:59:12.000Z (over 1 year ago)
- Last Synced: 2024-04-12T16:24:43.152Z (over 1 year ago)
- Topics: bayesian-inference, bilby, machine-learning, nested-sampling, normalizing-flows, python, pytorch
- Language: Python
- Homepage: https://nessai.readthedocs.io/
- Size: 3.05 MB
- Stars: 31
- Watchers: 4
- Forks: 3
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
Awesome Lists containing this project
README
[](https://doi.org/10.5281/zenodo.4550693)
[](https://pypi.org/project/nessai/)
[](https://anaconda.org/conda-forge/nessai)
[](https://nessai.readthedocs.io/en/latest/?badge=latest)
[](https://codecov.io/gh/mj-will/nessai)
[](https://app.gitter.im/#/room/#nessai:gitter.im)
# nessai: Nested Sampling with Artificial Intelligence
``nessai`` (/ˈnɛsi/): Nested Sampling with Artificial Intelligence
``nessai`` is a nested sampling algorithm for Bayesian Inference that incorporates normalising flows. It is designed for applications where the Bayesian likelihood is computationally expensive.
## Installation
``nessai`` can be installed using ``pip``:
```console
pip install nessai
```
or via ``conda``
```console
conda install -c conda-forge -c pytorch nessai
```
### PyTorch
By default the version of PyTorch will not necessarily match the drivers on your system, to install a different version with the correct CUDA support see the PyTorch homepage for instructions: https://pytorch.org/.
### Using ``bilby``
As of ``bilby`` version 2.3.0, the recommended way to use ``nessai`` is via the [``nessai-bilby`` sampler plugin](https://github.com/bilby-dev/nessai-bilby).
This can be installed via either ``conda`` or ``pip`` and provides the most
up-to-date interface for ``nessai``.
This includes support for the importance nested sampler (``inessai``).
It can be installed using either
```console
pip install nessai-bilby
```
or
```console
conda install -c conda-forge nessai-bilby
```
See the examples included with ``nessai`` for how to run ``nessai`` via ``bilby``.
## Documentation
Documentation is available at: [nessai.readthedocs.io](https://nessai.readthedocs.io/)
## Help
For questions and other support, please either use our [gitter room](https://app.gitter.im/#/room/#nessai:gitter.im) or [open an issue](https://github.com/mj-will/nessai/issues/new/choose).
## Contributing
Please see the guidelines [here](https://github.com/mj-will/nessai/blob/master/CONTRIBUTING.md).
## Acknowledgements
The core nested sampling code, model design and code for computing the posterior in ``nessai`` was based on [`cpnest`](https://github.com/johnveitch/cpnest) with permission from the authors.
The normalising flows implemented in ``nessai`` are all either directly imported from [`nflows`](https://github.com/bayesiains/nflows/tree/master/nflows) or heavily based on it.
Other code snippets that draw on existing code reference the source in their corresponding doc-strings.
The authors also thank Christian Chapman-Bird, Laurence Datrier, Fergus Hayes, Jethro Linley and Simon Tait for their feedback and help finding bugs in ``nessai``.
## Citing
If you find ``nessai`` useful in your work please cite the DOI for this code and our papers:
```bibtex
@software{nessai,
author = {Michael J. Williams},
title = {nessai: Nested Sampling with Artificial Intelligence},
month = feb,
year = 2021,
publisher = {Zenodo},
version = {latest},
doi = {10.5281/zenodo.4550693},
url = {https://doi.org/10.5281/zenodo.4550693}
}
@article{Williams:2021qyt,
author = "Williams, Michael J. and Veitch, John and Messenger, Chris",
title = "{Nested sampling with normalizing flows for gravitational-wave inference}",
eprint = "2102.11056",
archivePrefix = "arXiv",
primaryClass = "gr-qc",
doi = "10.1103/PhysRevD.103.103006",
journal = "Phys. Rev. D",
volume = "103",
number = "10",
pages = "103006",
year = "2021"
}
@article{Williams:2023ppp,
author = "Williams, Michael J. and Veitch, John and Messenger, Chris",
title = "{Importance nested sampling with normalising flows}",
eprint = "2302.08526",
archivePrefix = "arXiv",
primaryClass = "astro-ph.IM",
reportNumber = "LIGO-P2200283",
month = "2",
year = "2023"
}
```