dynesty
Nested sampling and dynamic nested sampling for Bayesian evidence and posterior exploration.
External resource. No execution or independent verification is claimed here.
How accurately does nested sampling estimate evidence for a fixed bounded Gaussian likelihood?
Compute the reference integral analytically, fix the prior and stopping protocol, and compare repeated estimates and uncertainty intervals without selecting successful seeds.
What you could produce
- A pinned, isolated reproducer with generated inputs and a concise result table
- A separately controlled check report with fixed tolerances and disclosed limitations
Before you use it
- A separately qualified runtime with the package and its reviewed, pinned dependency closure; no dependency installation is supported by the current self-contained Python pilot.
Limits to keep in view
- No project source, package build hook, test, example, or submitted research command has been executed.
- The documented pilot supports self-contained Python 3.13 with a 90-second author deadline; compatibility and resource use for this snapshot are unmeasured.
Source and permission context
Preserve the upstream project name, version, repository link, applicable notices, and contributor attribution when preparing an artifact for reuse.
Catalog listing reviewed. This review covers the description and source links displayed here.
Approved for catalog metadata and links. The displayed entry identifies dynesty with the concise collection-authored summary “Nested sampling and dynamic nested sampling for Bayesian evidence and posterior exploration.” and points to the public upstream repository https://github.com/joshspeagle/dynesty. The wording describes function and possible investigation without reproducing upstream source or documentation, claiming execution, or implying endorsement or rights beyond the recorded scopes.
Reviewed 2026-09-14. Copying or adapting source files remains subject to their own terms.
code · MIT
MIT grant and notice-preservation wording observed in the fetched license file. Observation is limited to LICENSE at commit d8affbcd18d1cb894e0c7102ba31c65794461b55; it is not a repository-wide rights clearance.
Inspect the license evidence ↗Before copying source material
- Bundled datasets, examples, submodules, vendored code, and dependency licenses have not been audited; the observed top-level license does not clear all of them.
- README and documentation rights were not independently resolved from the main code license; this catalog only links and describes.