{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:YFITKEHIIWHPOAMUWAA6VHHH5G","short_pith_number":"pith:YFITKEHI","canonical_record":{"source":{"id":"1907.10109","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-07-23T19:36:27Z","cross_cats_sorted":["stat.AP","stat.CO"],"title_canon_sha256":"b54a12c5bf7bf7b140f8f833f94f57ca4cf2b2e29280a8fad2cf29cdaa5925aa","abstract_canon_sha256":"74e2044bac156ab63531162cc67035ffd7f5d0bdd12985fcc570c451fadb612e"},"schema_version":"1.0"},"canonical_sha256":"c1513510e8458ef70194b001ea9ce7e9b1ee35b2c69bb24364fb9219e2eb6e59","source":{"kind":"arxiv","id":"1907.10109","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.10109","created_at":"2026-05-17T23:39:39Z"},{"alias_kind":"arxiv_version","alias_value":"1907.10109v1","created_at":"2026-05-17T23:39:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.10109","created_at":"2026-05-17T23:39:39Z"},{"alias_kind":"pith_short_12","alias_value":"YFITKEHIIWHP","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"YFITKEHIIWHPOAMU","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"YFITKEHI","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:YFITKEHIIWHPOAMUWAA6VHHH5G","target":"record","payload":{"canonical_record":{"source":{"id":"1907.10109","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-07-23T19:36:27Z","cross_cats_sorted":["stat.AP","stat.CO"],"title_canon_sha256":"b54a12c5bf7bf7b140f8f833f94f57ca4cf2b2e29280a8fad2cf29cdaa5925aa","abstract_canon_sha256":"74e2044bac156ab63531162cc67035ffd7f5d0bdd12985fcc570c451fadb612e"},"schema_version":"1.0"},"canonical_sha256":"c1513510e8458ef70194b001ea9ce7e9b1ee35b2c69bb24364fb9219e2eb6e59","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:39.030199Z","signature_b64":"6lsNz30iXrY+D1Al6LL1QgOq9gr17Sx26TmkphLGPLibkRvDb2QHbGwjec8UvpqI0iAdIHU1zEIL52SJgD++Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c1513510e8458ef70194b001ea9ce7e9b1ee35b2c69bb24364fb9219e2eb6e59","last_reissued_at":"2026-05-17T23:39:39.029707Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:39.029707Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.10109","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E2BmT6GFqogfzvJgNHOrRFTsSy0o6fhMd1/KFZXoOd6IIm+DmfSoVIgU7GGFbGbL04KsETuKdXRVQRS/308NAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T22:09:11.172418Z"},"content_sha256":"c13925bcc0646009bb258de64655815f75a4d9578428e84fc150522deafea489","schema_version":"1.0","event_id":"sha256:c13925bcc0646009bb258de64655815f75a4d9578428e84fc150522deafea489"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:YFITKEHIIWHPOAMUWAA6VHHH5G","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Conjugate Nearest Neighbor Gaussian Process Models for Efficient Statistical Interpolation of Large Spatial Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.CO"],"primary_cat":"stat.ME","authors_text":"Andrew O. Finley, Bruce D. Cook, Shinichiro Shirota, Sudipto Banerjee","submitted_at":"2019-07-23T19:36:27Z","abstract_excerpt":"A key challenge in spatial statistics is the analysis for massive spatially-referenced data sets. Such analyses often proceed from Gaussian process specifications that can produce rich and robust inference, but involve dense covariance matrices that lack computationally exploitable structures. The matrix computations required for fitting such models involve floating point operations in cubic order of the number of spatial locations and dynamic memory storage in quadratic order. Recent developments in spatial statistics offer a variety of massively scalable approaches. Bayesian inference and hi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.10109","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b7qibYnD7Vt5/00QDX7a3dOtyZMRcYYifXi60UvfUxqKV3Rdc6K6OA4IuMTYSpyAUbbGKVatcGKlbYNXxC53AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T22:09:11.173051Z"},"content_sha256":"e23477a879031d035561252e6dd9a5a5b1d2c4d441d46aa19428fd55518ea846","schema_version":"1.0","event_id":"sha256:e23477a879031d035561252e6dd9a5a5b1d2c4d441d46aa19428fd55518ea846"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YFITKEHIIWHPOAMUWAA6VHHH5G/bundle.json","state_url":"https://pith.science/pith/YFITKEHIIWHPOAMUWAA6VHHH5G/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YFITKEHIIWHPOAMUWAA6VHHH5G/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-27T22:09:11Z","links":{"resolver":"https://pith.science/pith/YFITKEHIIWHPOAMUWAA6VHHH5G","bundle":"https://pith.science/pith/YFITKEHIIWHPOAMUWAA6VHHH5G/bundle.json","state":"https://pith.science/pith/YFITKEHIIWHPOAMUWAA6VHHH5G/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YFITKEHIIWHPOAMUWAA6VHHH5G/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:YFITKEHIIWHPOAMUWAA6VHHH5G","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"74e2044bac156ab63531162cc67035ffd7f5d0bdd12985fcc570c451fadb612e","cross_cats_sorted":["stat.AP","stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-07-23T19:36:27Z","title_canon_sha256":"b54a12c5bf7bf7b140f8f833f94f57ca4cf2b2e29280a8fad2cf29cdaa5925aa"},"schema_version":"1.0","source":{"id":"1907.10109","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.10109","created_at":"2026-05-17T23:39:39Z"},{"alias_kind":"arxiv_version","alias_value":"1907.10109v1","created_at":"2026-05-17T23:39:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.10109","created_at":"2026-05-17T23:39:39Z"},{"alias_kind":"pith_short_12","alias_value":"YFITKEHIIWHP","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"YFITKEHIIWHPOAMU","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"YFITKEHI","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:e23477a879031d035561252e6dd9a5a5b1d2c4d441d46aa19428fd55518ea846","target":"graph","created_at":"2026-05-17T23:39:39Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"A key challenge in spatial statistics is the analysis for massive spatially-referenced data sets. Such analyses often proceed from Gaussian process specifications that can produce rich and robust inference, but involve dense covariance matrices that lack computationally exploitable structures. The matrix computations required for fitting such models involve floating point operations in cubic order of the number of spatial locations and dynamic memory storage in quadratic order. Recent developments in spatial statistics offer a variety of massively scalable approaches. Bayesian inference and hi","authors_text":"Andrew O. Finley, Bruce D. Cook, Shinichiro Shirota, Sudipto Banerjee","cross_cats":["stat.AP","stat.CO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-07-23T19:36:27Z","title":"Conjugate Nearest Neighbor Gaussian Process Models for Efficient Statistical Interpolation of Large Spatial Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.10109","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c13925bcc0646009bb258de64655815f75a4d9578428e84fc150522deafea489","target":"record","created_at":"2026-05-17T23:39:39Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"74e2044bac156ab63531162cc67035ffd7f5d0bdd12985fcc570c451fadb612e","cross_cats_sorted":["stat.AP","stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-07-23T19:36:27Z","title_canon_sha256":"b54a12c5bf7bf7b140f8f833f94f57ca4cf2b2e29280a8fad2cf29cdaa5925aa"},"schema_version":"1.0","source":{"id":"1907.10109","kind":"arxiv","version":1}},"canonical_sha256":"c1513510e8458ef70194b001ea9ce7e9b1ee35b2c69bb24364fb9219e2eb6e59","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c1513510e8458ef70194b001ea9ce7e9b1ee35b2c69bb24364fb9219e2eb6e59","first_computed_at":"2026-05-17T23:39:39.029707Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:39.029707Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6lsNz30iXrY+D1Al6LL1QgOq9gr17Sx26TmkphLGPLibkRvDb2QHbGwjec8UvpqI0iAdIHU1zEIL52SJgD++Bw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:39.030199Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.10109","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c13925bcc0646009bb258de64655815f75a4d9578428e84fc150522deafea489","sha256:e23477a879031d035561252e6dd9a5a5b1d2c4d441d46aa19428fd55518ea846"],"state_sha256":"2ad0d0625700b68cce1350c74d3270bd7b4ab6804f12658bac9ddb9d6ecf6689"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nFxnswSm7fbDuMfl7T8vA/rX6xcV7W/0I/AKd67emKhr5zbmrOi1ZnFQbBSVMeMqF6C4DnuzA2WMeoXralJDDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T22:09:11.176412Z","bundle_sha256":"0305b41c98b8e426c5e49a959f8e8ebf4907f53857a4bdb3ce4888b3712266aa"}}