Pith Number

pith:J3GDWA3P

pith:2026:J3GDWA3PTB3XN2XBTL3SBGMQMS

not attested not anchored not stored refs pending

Fast and accurate conditioning for large-scale and online Gaussian process prediction problems

Christopher J. Geoga, Samanyu Arora

Conditioning on a small number of carefully designed linear combinations of observations recovers machine-precision exact conditional distributions for Gaussian process prediction.

arxiv:2605.02574 v2 · 2026-05-04 · stat.CO · cs.NA · math.NA · stat.ME

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\usepackage{pith}
\pithnumber{J3GDWA3PTB3XN2XBTL3SBGMQMS}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ 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.

Claims

C1strongest claim

For kernel functions that are smooth away from the origin, conditioning on a small number r of such data contrasts can be machine-precision accurate for the full exact conditional distributions.

C2weakest assumption

The kernel functions are smooth away from the origin and that the linear combinations (contrasts) can be carefully designed to achieve the claimed accuracy and efficiency.

C3one line summary

Conditioning on a small number of carefully designed linear combinations of data enables machine-precision accurate Gaussian process predictions at low cost for large-scale and online problems.

Receipt and verification

First computed	2026-05-29T01:05:11.466163Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

4ecc3b036f987776eae19af7209990648b526c33c828cf0dbd9432b754248e69

Aliases

arxiv: 2605.02574 · arxiv_version: 2605.02574v2 · doi: 10.48550/arxiv.2605.02574 · pith_short_12: J3GDWA3PTB3X · pith_short_16: J3GDWA3PTB3XN2XB · pith_short_8: J3GDWA3P

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/J3GDWA3PTB3XN2XBTL3SBGMQMS \
  | 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: 4ecc3b036f987776eae19af7209990648b526c33c828cf0dbd9432b754248e69

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "cd4991c15cecc8a5db453dfa3ffb0845aefc933ecab835cc5992f14c4ec8b6a7",
    "cross_cats_sorted": [
      "cs.NA",
      "math.NA",
      "stat.ME"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "stat.CO",
    "submitted_at": "2026-05-04T13:29:09Z",
    "title_canon_sha256": "9bcf208cdd62a13bfc196ae8049b969e116f1d51aa5fce813bcd7840c5acf7ba"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.02574",
    "kind": "arxiv",
    "version": 2
  }
}