A dynamic nested sampling package for computing Bayesian posteriors and evidences.
Project description
A Dynamic Nested Sampling package for computing Bayesian posteriors and evidences. Pure Python. MIT license.
### Documentation Documentation can be found [here](https://dynesty.readthedocs.io).
### Installation The most stable release of dynesty can be installed through [pip](https://pip.pypa.io/en/stable) via ` pip install dynesty ` The current (less stable) development version can be installed by running ` python setup.py install ` from inside the repository.
### Demos Several Jupyter notebooks that demonstrate most of the available features of the code can be found [here](https://github.com/joshspeagle/dynesty/tree/master/demos).
### Attribution
Please cite [Speagle (2019)](https://arxiv.org/abs/1904.02180) if you find the package useful in your research, along with any relevant papers on the [citations page](https://dynesty.readthedocs.io/en/latest/index.html#citations).
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