nessai
======

``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.

The code is available at: https://github.com/mj-will/nessai.

For questions or 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>`_.

.. toctree::
   :maxdepth: 1
   :caption: User guide

   installation
   running-the-sampler
   outputs
   sampler-configuration
   importance-nested-sampling
   reparameterisations
   normalising-flows-configuration
   parallelisation
   gravitational-wave-inference
   discrete-parameters
   plugins
   further-details
   faqs
   API reference </autoapi/nessai/index>

.. toctree::
   :maxdepth: 1
   :caption: Examples

   gaussian-example
   bilby-example

Citing nessai
=============

If you find ``nessai`` useful in your work please cite the DOI for this code and our paper:

.. code-block:: 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",
      doi = "10.1088/2632-2153/acd5aa",
      journal = "Mach. Learn. Sci. Tech.",
      volume = "4",
      number = "3",
      pages = "035011",
      year = "2023"
   }