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uai-experiments
Use when designing or auditing UAI experiments where inference quality is the endpoint, covering calibration and coverage measurement, posterior diagnostics, structure-recovery metrics for causal and graphical models, seeded repeated runs, baselines from both sampling and optimization families, and mapping each claim to its evidence.
UAI-Skills/skills/uai-experiments/SKILL.md
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# UAI Experiments
Use this while the empirical design is still changeable. At UAI the object under test is
usually an inference procedure — a posterior, a graph, an interval, a decision policy —
so the experimental question is rarely "is accuracy higher?" and usually "is the
uncertainty right, and at what cost?". Reviewers score whether claims are backed up
convincingly; design the study so each claim has a designated exhibit.
## Metrics follow the probabilistic object
| Claimed object | Primary metrics | Supporting diagnostics |
|---|---|---|
| Posterior approximation | Wasserstein/KL to gold-standard posterior on tractable cases | R-hat, ESS, trace plots; ELBO with restarts |
| Predictive uncertainty | NLL, CRPS, empirical coverage vs nominal | Reliability diagrams; ECE with stated binning |