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
pip install .
from inside the repository.
Several Jupyter notebooks that demonstrate most of the available features of the code can be found here.
If you use this package in your research, please cite both of these references:
- The original paper Speagle (2020)
- The Python implementation Koposov et al. (2024) (the citation information is at the bottom right of the linked page)
Please also consider citing 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.