nessai


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# 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 1.1.0, ``nessai`` is now supported by default but it is still an optional requirement. See the [``bilby`` documentation](https://lscsoft.docs.ligo.org/bilby/index.html) for installation instructions for `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/)


## 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"
}

```

            

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    "description": "[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4550693.svg)](https://doi.org/10.5281/zenodo.4550693)\n[![PyPI](https://img.shields.io/pypi/v/nessai)](https://pypi.org/project/nessai/)\n[![Conda Version](https://img.shields.io/conda/vn/conda-forge/nessai.svg)](https://anaconda.org/conda-forge/nessai)\n[![Documentation Status](https://readthedocs.org/projects/nessai/badge/?version=latest)](https://nessai.readthedocs.io/en/latest/?badge=latest)\n![license](https://anaconda.org/conda-forge/nessai/badges/license.svg)\n![tests](https://github.com/mj-will/nessai/actions/workflows/tests.yml/badge.svg)\n![int-tests](https://github.com/mj-will/nessai/actions/workflows/integration-tests.yml/badge.svg)\n[![codecov](https://codecov.io/gh/mj-will/nessai/branch/main/graph/badge.svg?token=O7SN167SK6)](https://codecov.io/gh/mj-will/nessai)\n\n# nessai: Nested Sampling with Artificial Intelligence\n\n``nessai`` (/\u02c8n\u025bsi/): Nested Sampling with Artificial Intelligence\n\n``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.\n\n## Installation\n\n``nessai`` can be installed using ``pip``:\n\n```console\npip install nessai\n```\n\nor via ``conda``\n\n```console\nconda install -c conda-forge -c pytorch nessai\n```\n\n### PyTorch\n\nBy 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/.\n\n### Using ``bilby``\n\nAs of `bilby` version 1.1.0, ``nessai`` is now supported by default but it is still an optional requirement. See the [``bilby`` documentation](https://lscsoft.docs.ligo.org/bilby/index.html) for installation instructions for `bilby`\n\nSee the examples included with ``nessai`` for how to run ``nessai`` via ``bilby``.\n\n## Documentation\n\nDocumentation is available at: [nessai.readthedocs.io](https://nessai.readthedocs.io/)\n\n\n## Contributing\n\nPlease see the guidelines [here](https://github.com/mj-will/nessai/blob/master/CONTRIBUTING.md).\n\n\n## Acknowledgements\n\nThe 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.\n\nThe 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.\n\nOther code snippets that draw on existing code reference the source in their corresponding doc-strings.\n\nThe authors also thank Christian Chapman-Bird, Laurence Datrier, Fergus Hayes, Jethro Linley and Simon Tait for their feedback and help finding bugs in ``nessai``.\n\n## Citing\n\nIf you find ``nessai`` useful in your work please cite the DOI for this code and our papers:\n\n```bibtex\n@software{nessai,\n  author       = {Michael J. Williams},\n  title        = {nessai: Nested Sampling with Artificial Intelligence},\n  month        = feb,\n  year         = 2021,\n  publisher    = {Zenodo},\n  version      = {latest},\n  doi          = {10.5281/zenodo.4550693},\n  url          = {https://doi.org/10.5281/zenodo.4550693}\n}\n\n@article{Williams:2021qyt,\n    author = \"Williams, Michael J. and Veitch, John and Messenger, Chris\",\n    title = \"{Nested sampling with normalizing flows for gravitational-wave inference}\",\n    eprint = \"2102.11056\",\n    archivePrefix = \"arXiv\",\n    primaryClass = \"gr-qc\",\n    doi = \"10.1103/PhysRevD.103.103006\",\n    journal = \"Phys. Rev. D\",\n    volume = \"103\",\n    number = \"10\",\n    pages = \"103006\",\n    year = \"2021\"\n}\n\n@article{Williams:2023ppp,\n    author = \"Williams, Michael J. and Veitch, John and Messenger, Chris\",\n    title = \"{Importance nested sampling with normalising flows}\",\n    eprint = \"2302.08526\",\n    archivePrefix = \"arXiv\",\n    primaryClass = \"astro-ph.IM\",\n    reportNumber = \"LIGO-P2200283\",\n    month = \"2\",\n    year = \"2023\"\n}\n\n```\n",
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