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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[![gitter](https://img.shields.io/gitter/room/nessai/nessai)](https://app.gitter.im/#/room/#nessai:gitter.im)\n\n# nessai: Nested Sampling with Artificial Intelligence\n\n``nessai`` (/ˈnɛsi/): 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 2.3.0, the recommended way to use ``nessai`` is via the [``nessai-bilby`` sampler plugin](https://github.com/bilby-dev/nessai-bilby).\nThis can be installed via either ``conda`` or ``pip`` and provides the most\nup-to-date interface for ``nessai``.\nThis includes support for the importance nested sampler (``inessai``).\n\nIt can be installed using either\n\n```console\npip install nessai-bilby\n```\n\nor\n\n```console\nconda install -c conda-forge nessai-bilby\n```\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## Help\n\nFor 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).\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","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmj-will%2Fnessai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmj-will%2Fnessai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmj-will%2Fnessai/lists"}