dynesty is a Pure Python, MIT-licensed Dynamic Nested Sampling package for estimating Bayesian posteriors
and evidences. See Crash Course and Getting Started
for more information. The latest development version can be found here.
The release paper describing the code can be found here.
dynesty is compatible with both Python 2.7 and Python 3.6. It requires
numpy (for arithmetic),
scipy (for special functions),
matplotlib (for plotting), and
six (to enforce Python 2/3 compliance).
While not required,
tqdm also allows for a nice progress bar.
Installing the most recent stable version of the package is as easy as:
pip install dynesty
Alternately, for users who might want newer development versions, it can also be installed directly from a local copy of the repository by running:
python setup.py install
If you find
dynesty useful in your research, please cite
Speagle (2019). You are
also encouraged to cite:
- Nested Sampling: Skilling (2004) and Skilling (2006).
- Dynamic Nested Sampling: Higson et al. (2017b).
You are also encouraged to cite the following papers as relevant:
- Single ellipsoid bound: Mukherjee, Parkinson & Liddle (2006).
- Multiple ellipsoid bounds: Feroz, Hobson & Bridges (2009).
- Overlapping balls/cubes: Buchner (2016) and Buchner (2017).
- Random walks/staggers: Skilling (2006).
- Multivariate/Random slice sampling: Neal (2003), Handley, Hobson & Lasenby (2015a), and Handley, Hobson & Lasenby (2015b).
- Hamiltonian/Reflective slice sampling: Neal (2003), Skilling (2012), and Feroz & Skilling (2013).
- Nested Sampling error analysis: Chopin & Robert (2010) and Higson et al. (2017a).
See References and Acknowledgements for additional details.
- Ensemble bounds can now adapt to elongated distributions (with Johannes Buchner).
- Random walks now behave differently near boundaries (with Gregory Ashton).
- Pickling sampler states should now work better in Python 3 (with Dustin Lang.
- Doubled output errors in default approximation in line with theoretical expectations.
- Small bugfixes and docfixes (with Patricio Cubillos).
- Added support for periodic boundary conditions.
- Set up basic tests for continuous integration.
- Added a logo!
- Updated and reorganized documentation and demos.
- Added proper support for gradients.
- Changed defaults and added several “quality of life” improvements.
- Updated documentation.
- Modified re-scaling behavior to better deal with inefficient proposals.
- Improved stability of the current ellipsoid decomposition algorithm.
- Added new
'auto'options and changed a number of defaults to make things easier for general users.
- Plotting now defaults to 95% credible intervals instead of 68%.
- Added in a fast approximation option for
- Modified the default stopping heuristic. It now evaluates significantly faster but is a less accurate probe of the “true” KL divergence.
'rwalk'behavior to better deal with edge cases.
- Changed defaults so performance should now be more stable (albiet slower) for the average user.
- Improved the stability of bounding ellipsoids.
- Fixed performance issues with
- Small plotting improvements.
- Fixed a minor bootstrapping bug that affected performance for some users.
- Fixed a serious bug associated with the new singular decomposition algorithm and changed its behavior so it no longer auto-kills user runs when it fails.
dynestyis now on PyPI!
- Added two new slice sampling options (
- Changed internals to allow user to access quantities during dynamic batch allocation. WARNING: Breaks some aspects of backwards compatibility for advanced users utilizing generators.
- Simplified parallelism options.
- Fixed a singular decomposition bug that occasionally appeared during runtime.
- Small plotting/utility improvements.
- Fixed additional Python 2/3 compatibility bugs.
- Added the ability to pass user-specified custom print functions.
- Added importance reweighting.
- Small improvements to plotting utilities.
- Small changes to improve user outputs and basic functionality.
mapbugs that broke compatibility between Python 2 and 3.
- Fixed a bug where the sampler could break during the first update from the
unit cube when using a
- Introduced a function wrapper for
loglikelihoodfunctions to allow users to pass
- Fixed a small bug that could cause bounding ellipsoids to fail.
- Introduced a stability fix to the default
weight_functionwhen computing evidence-based weights.
Initial beta release.