Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
Results and benchmarks
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC presents a bayesian probability approach for cross-validation.
Benchmark evidence is limited
Evidence graph: 2 refs, 1 links.
Utility signals: depth 65/100, grounding 58/100, status medium.
Implementation
No direct implementation yet
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Use the implementation status and reproduction sections for the current action plan.
No verified maintained repo yet
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- No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.
- Start from related paper: Efficient implementation of leave-one-out cross-validation and WAIC for evaluating fitted Bayesian models.
- Start from this likely method family: Bayesian probability.
Time to first repro: a few days
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- Estimate is based on paper-only reproduction flow
Reproduction readiness
No repo
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Hardware requirements
- Expect multi-day setup/compute for meaningful reproduction based on current guidance.
Validation caveat
Hugging Face artifacts
No trustworthy direct or curated related Hugging Face artifacts were found yet. Use targeted searches to quickly locate candidate models, datasets, and demos.
Tip: start with models, then check datasets and spaces if you need evaluation data or demos.
Research context
5,600
Citations
45
References
Tasks
Cross-validation, Computer science, Data mining, Econometrics, leave-one-out cross-validation, WAIC, importance weights, Stan
Methods
Bayesian probability, Model validation, Variable-order Bayesian network, Bayesian inference, model comparison
Domains
Artificial intelligence, Machine learning, Mathematics
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