Pith Number
pith:Y4FKDMLJ
pith:2016:Y4FKDMLJLWFIVYVNITX433F7DJ
not attested
not anchored
not stored
refs pending
Learning Gaussian Graphical Models With Fractional Marginal Pseudo-likelihood
arxiv:1602.07863 v1 · 2016-02-25 · stat.ML · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{Y4FKDMLJLWFIVYVNITX433F7DJ}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
· sign in to
claim
4
Citations
5
Replications
✓
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 | 2026-05-18T00:46:31.985746Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
c70aa1b1695d8a8ae2ad44efcdecbf1a7581956f1d95552b09d2f348e6253aa7
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/Y4FKDMLJLWFIVYVNITX433F7DJ \
| 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: c70aa1b1695d8a8ae2ad44efcdecbf1a7581956f1d95552b09d2f348e6253aa7
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "2490a6c2baada51c28e0696a0e49333fa6daefe218ddc53f2934dd7db83f2db9",
"cross_cats_sorted": [
"cs.LG"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "stat.ML",
"submitted_at": "2016-02-25T09:42:46Z",
"title_canon_sha256": "4a4e2fa1d7ae9ee7f821cf20f23ccc74b61d2cec25ac19cc5d55c2b4e2b85c22"
},
"schema_version": "1.0",
"source": {
"id": "1602.07863",
"kind": "arxiv",
"version": 1
}
}