Nessai: Nested Sampling with Aritificial Intelligence
Project description
Nessai: Nested Sampling with Artificial Intelligence
nessai
(/ˈnɛsi/): Nested Sampling with Aritificial Intelligence
nessai
is a nested sampling algorithm for Bayesian Inference that incorporates normalisings flows. It is designed for applications where the Bayesian likelihood is computationally expensive.
Installation
nessai
can be installed using pip
:
$ pip install nessai
Installing via conda
is not currently supported.
PyTorch
By default the version of PyTroch 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/.
Adding Bilby
This package requieres a fork of Bilby that includes the sampler, it can be installed by running (this requires pip):
$ pip install git+https://git.ligo.org/michael.williams/bilby.git@add-nessai-sampler#egg=bilby
Documentation
Documenation is available at: nessai.readthedocs.io
Contributing
Please see the guidelines here.
Acknowledgements
The core nested sampling code, model design and code for computing the posterior in nessai
was based on cpnest
with permission from the authors.
The normalising flows implemented in nessai
are all either directly imported from nflows
or heavily based on it.
Other code snippets that draw on existing code reference the source in their corresponding doc-strings.
Citing
If you find nessai
useful in your work please cite the DOI for this code and our paper:
@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{williams2021nested,
title={Nested Sampling with Normalising Flows for Gravitational-Wave Inference},
author={Michael J. Williams and John Veitch and Chris Messenger},
year={2021},
eprint={2102.11056},
archivePrefix={arXiv},
primaryClass={gr-qc}
}
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