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