pith:BWTFCH7V
Optimal Spatio-Temporal Decoupling for Bayesian Conformal Prediction
State-Adaptive Bayesian Conformal Prediction gates temporal inertia with spatial kernel-density evidence to balance coverage and efficiency.
arxiv:2605.00432 v2 · 2026-05-01 · cs.LG · stat.ML
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Record completeness
Claims
We rigorously prove this mechanism's optimality, identifying a minimax bias-variance tradeoff governed by an evidence threshold K. ... SA-BCP resolves the systematic under-coverage inherent to ACI variants while simultaneously reducing the uncalibrated interval bloat of Bayesian CP by 10% to 37% under high-confidence requests.
That spatial kernel-density evidence can accurately and proactively identify historical regimes to gate temporal inertia without introducing new lag or calibration errors, as stated in the description of the SA-BCP mechanism.
SA-BCP achieves optimal spatio-temporal decoupling in Bayesian conformal prediction by gating temporal inertia with spatial kernel-density evidence, governed by a minimax bias-variance threshold K, and outperforms ACI and Bayesian CP baselines on financial datasets.
Receipt and verification
| First computed | 2026-06-12T00:07:49.195151Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
0da6511ff53009458a4a3c624b4fa2c570979d4f815d24e6e03de6ec7fb6a1f5
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/BWTFCH7VGAEULCSKHRREWT5CYV \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 0da6511ff53009458a4a3c624b4fa2c570979d4f815d24e6e03de6ec7fb6a1f5
Canonical record JSON
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