More guides on how to interpret chain results and debugging MCMC
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 35/100
- Issue type
- Documentation
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- julia
- Domain
- documentation, machine-learning
Research direction
Start by reviewing the FAQ discussion in issue #437 and the linked Julia Discourse and Cross Validated discussions. Prepare guides explaining mcse, ess_bulk, ess_tail, rhat, and MCMC debugging, with the work complete when the requested summary and debugging guidance is documented for readers.
Written by the indexing model from the issue text.
Description
Transferring this to a new issue since the FAQ issue where this was posted is probably too broad
Another one I'm running into that could probably use a convenient FAQ response is "How do I interpret the summary statistics?" e.g.
mcse,ess_bulk,ess_tail,rhatand so on. As someone with a maths background NOT in stats/probability having that additional context is useful.Upon some initial search, some people have done a bit of digging already:
- https://discourse.julialang.org/t/what-is-the-interpretation-of-turings-std-naive-se-mcse/52252
- https://stats.stackexchange.com/questions/348984/stan-hatr-versus-gelman-rubin-hatr-definition
In an ideal scenario I'll probably dig through everything and then draft a summary to submit as a pull request, but it's likely I'll forget, hence my writing it here as a suggestion.
Originally posted by @matthras in #437
- Dominant language
- Markdown
- Stars
- 238
- Forks
- 107
- Avg merge
- 1d 1h
- Merged PRs (30d)
- 2
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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