A dynamic nested sampling package for computing Bayesian posteriors and evidences.
Project description
![dynesty in action](https://github.com/joshspeagle/dynesty/blob/master/docs/images/title.gif)
A Dynamic Nested Sampling package for computing Bayesian posteriors and evidences. Pure Python. MIT license. Beta release.
### 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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for dynesty-0.9.7-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 50cfe9ed818c401bfc2949d556beb0ff581b6abdbcfb0f9d838a5383654918c2 |
|
MD5 | 7db7976b0980f5adc851b9da09932697 |
|
BLAKE2b-256 | 400b78555fafdbfe9f13771fbedfd0aacf574c4eaf4325ba932c93fa883daa1b |