summary for standalone GQ, VI, etc.
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 35/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- python
- Domain
- data, developer-experience
Research direction
Start by inspecting the existing CmdStanMCMC.summary() method and the high-level summary helper, then compare the outputs exposed by CmdStanMCMC, CmdStanGQ, and CmdStanVB. Determine how draws are extracted and whether Laplace approximation is supported; done means the supported sampling results have a consistent summary path and the Laplace behavior is explicitly resolved.
Written by the indexing model from the issue text.
Description
Summary
Any object returned by sampling should allow .summary() to be called on it to report mean, sd, MCMC SE, quantiles, and R-hat. This includes objects returned by
CmdStanMCMC: MCMC samplingCmndStanGQ: generated quantitiesCmdStanVB: variational inference- ???: Laplace approximation
Is Laplace approximation not supported yet in CmdStanPy? It will also return a sample of multiple draws and should also include a .summary() method.
Description
One way to do this would be to have each of these wrapper objects allow the actual draws to be extracted. Right now, there is a high-level "helper" function, where if mcmc_fit is a CmdStanMCMC object, I just call mcmc_fit.summary() directly. I would rather have this work by mcmc_fit.draws() pulling out a simple draws object on which the summary() operates. Then the other object would also support a .draws() extraction and then summary() would be a standalone function that applies to a draws object rather than to the whole output of a run (I don't know what else is in the CmdStanMCMC object---I only ever use the draws).
Current Version
1.1.0
- Dominant language
- Python
- Stars
- 198
- Forks
- 81
- PR merge metrics
- No merged PRs in 30d
Contributor guide
First steps
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