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Dynamic Nested Sampling package for computing Bayesian posteriors and evidences


1 month ago

This is mostly a bug fix release. Some changes to bounding strategy. Some warnings were added. See more details in the changelog.


5 months ago

This is a bug release mostly concerning the checkpointing. Check the changelog for more details.


7 months ago

This is a minor bug fix release with some other small changes.

The changes concern initialisation of live-points in the beginning of the run, boundary updates and resuming of runs. See the changelog for more details.


9 months ago

This a major release with a significant change of the sampler. It is now able to correctly sample likelihood functions with plateaus, (i.e likelihood functions with areas where likelihood value is exactly the same).

The previous behaviour also led to inaccurate log(Z) estimates if some fraction of the prior volume had log(P)=-LARGENUMBER


11 months ago

This is a release with a major performance bug fix for the dynamical sampler

A major performance regression has been discovered that lead to dynamic nested sampling batches becoming slower and slower. The regression was introduced in 1.2 and can easily lead to a factor of several slower performance if a large number of batches is used. ( see #415 ) Some small fixes related to resuming of interrupted sampling were applied as well.


1 year ago

Minor bug fix release.

  • When checkpointing is on the dynamic sampler will always checkpoint in the end of the run_nested() irrespective of checkpoint_time
  • Equally weighted samples are now randomly shuffled ( #408 )
  • The live_points option was somewhat broken when blob option was introduced requiring a tuple of 4 elements irrespective of whether your likelihood returns blobs or not. Now if you use blob=True and want to provide live_points you need to provide 4 elements (u,v,logl,blobs). If you use blob=False you will need to provide just 3 elements as before (u,v,logl)


1 year ago

Minor bug fix release.

  • Fix the non-working custom samplers
  • Fix the broken resume when using dynesty pool


1 year ago

This is a major release with several significant improvements.

  • The implementation of the check-points to save progress and allow restarting of fits.
  • A new simple interface to obtain equally weighted samples directly from results object.
  • Allow likelihood functions to return additional computed quantities (blobs) that will be saved together with samples.
  • Random slice sampling now supports interval doubling scheme which may increase the performance in the case of bad posteriors.

See the full changelog and links to the documentation of the new features here https://dynesty.readthedocs.io/en/v2.0.0/#changelog


1 year ago

Minor bug fix release.
See the changelog for details.


1 year ago

Bug fix release which addresses quite a serious bug which can lead to biased posteriors.

  • The problem with biased posteriors was fixed when using multi-ellipsoid bounds and rslice and rwalk samplers. Previously the chains did not satisfy detailed balance. (issue #364). Original discovery of the problem and help by Colm Talbot. In the case of complex posteriors, somewhat slower performance may be seen.
  • Fix the issue introduced in 1.2.1 when the prior_transform returns a tuple or or a list (rather than numpy array). Now that should be accepted.