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

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dynesty in action

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


Documentation can be found here.


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 install

from inside the repository.


Several Jupyter notebooks that demonstrate most of the available features of the code can be found here.


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

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.


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

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