dynesty 2.1.1

A dynamic nested sampling package for computing Bayesian posteriors and evidences.

dynesty

A Dynamic Nested Sampling package for computing Bayesian posteriors and evidences. Pure Python. MIT license.

Documentation

Documentation can be found here.

Installation

The most stable release of dynesty can be installed through pip 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.

Attribution

If you find the package useful in your research, please cite at least both of these references:

• The original paper Speagle (2020)
• The python implementation Koposov et al. (2023) (the citation info is at the bottom of the page on the right)

and ideally also papers describing the underlying methods (see the documentation for more details)

Reporting issues

If you want to report issues, or have questions, please do that on github.

Contributing

Patches and contributions are very welcome! Please see CONTRIBUTING.md for more details.