https://github.com/lacerbi/eissample

Ensemble (inversion) slice sampling: a robust, self-tuning MCMC method for posterior inference.
https://github.com/lacerbi/eissample

Last synced: 6 months ago
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Ensemble (inversion) slice sampling: a robust, self-tuning MCMC method for posterior inference.

• Host: GitHub
• URL: https://github.com/lacerbi/eissample
• Owner: lacerbi
• License: gpl-3.0
• Created: 2018-07-19T17:30:38.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2019-06-04T05:06:46.000Z (over 6 years ago)
• Last Synced: 2025-03-29T14:51:11.610Z (7 months ago)
• Language: MATLAB
• Size: 32.2 KB
• Stars: 3
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

          
# EISSample

*Ensemble inversion slice sampling* (EISS) is a robust, self-tuning MCMC method for posterior inference. The wider project is still work in progress; we make available here a simplified working version, `eissample_lite` that implements parallel slice sampling.

This algorithm was used for [this paper](https://github.com/lacerbi/visvest-causinf). Refer to that for a more detailed description.

In the meanwhile, please contact me at  for more information.