Pith Number
pith:GUVD2JL7
pith:2026:GUVD2JL7AI3GWP6C3WJX25XDAO
not attested
not anchored
not stored
refs pending
When can a posterior predictive check identify the learning rate? Exact degeneracy in Gaussian models and implications for Generalised Bayesian Inerence
arxiv:2606.07169 v1 · 2026-06-05 · stat.ME · math.ST · stat.TH
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{GUVD2JL7AI3GWP6C3WJX25XDAO}
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Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
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claim
4
Citations
5
Replications
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Portable graph bundle live · download bundle · merged
state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same
current state with the deterministic merge algorithm.
Receipt and verification
| First computed | 2026-06-08T01:04:50.463578Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
352a3d257f02366b3fc2dd937d76e30396bb0d6dc86d7963970381d77f070505
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/GUVD2JL7AI3GWP6C3WJX25XDAO \
| 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: 352a3d257f02366b3fc2dd937d76e30396bb0d6dc86d7963970381d77f070505
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "cb9fbd629db4cfc3e37ad01fb5ce12118bc485d3ede1315fb61a9e3abd16c5b5",
"cross_cats_sorted": [
"math.ST",
"stat.TH"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "stat.ME",
"submitted_at": "2026-06-05T11:38:48Z",
"title_canon_sha256": "b4fd6d44fc3b17298489fea4635bd2dc38bb04c5ca6eefa1a1d853414e239663"
},
"schema_version": "1.0",
"source": {
"id": "2606.07169",
"kind": "arxiv",
"version": 1
}
}