ArviZ
Diagnostics, summaries, and visualization support for Bayesian inference results.
External resource. No execution or independent verification is claimed here.
Which diagnostics distinguish deliberately separated chains from independently generated draws of the same target?
Construct labeled synthetic chain families with known generating rules, freeze diagnostic thresholds, and report detection and false-alert behavior rather than treating a diagnostic as proof of convergence.
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.
License evidence recorded. Review the scope and upstream conditions before reuse.
code · Apache-2.0
Apache License version 2.0 identified in the fetched license file. Observation is limited to LICENSE at commit d96624e7853595f4a583b5bd839d5dc118e785b7; it is not a repository-wide rights clearance.
Inspect the license evidence ↗Still unresolved
- 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.