Metadata-Version: 2.1
Name: nessai
Version: 0.2.2
Summary: Nessai: Nested Sampling with Aritificial Intelligence
Home-page: https://github.com/mj-will/nessai
Author: Michael J. Williams
Author-email: m.williams.4@research.gla.ac.uk
License: UNKNOWN
Project-URL: Documentation, https://nessai.readthedocs.io/
Keywords: nested sampling,normalising flows,machine learning
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: numpy (>=1.9)
Requires-Dist: pandas
Requires-Dist: matplotlib (>=2.0)
Requires-Dist: seaborn
Requires-Dist: scipy (>=0.16)
Requires-Dist: torch (>=1.7.0)
Requires-Dist: tqdm
Requires-Dist: nflows
Provides-Extra: dev
Requires-Dist: pytest ; extra == 'dev'
Requires-Dist: pytest-timeout ; extra == 'dev'
Requires-Dist: pytest-rerunfailures ; extra == 'dev'
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Requires-Dist: numpydoc ; extra == 'docs'
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# 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``:

```console
$ 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):

```console
$ pip install git+https://git.ligo.org/michael.williams/bilby.git@add-nessai-sampler#egg=bilby
```

## Documentation

Documenation 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.


