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 setup.py 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)
If you want to report issues, or have questions, please do that on github.
Patches and contributions are very welcome! Please see CONTRIBUTING.md for more details.
FAQs
A dynamic nested sampling package for computing Bayesian posteriors and evidences.
We found that dynesty demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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