{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:SKJAZHWGX3Z3Y2CSBFG4J6JVGU","short_pith_number":"pith:SKJAZHWG","canonical_record":{"source":{"id":"1506.06285","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-06-20T19:25:14Z","cross_cats_sorted":[],"title_canon_sha256":"a07ab7771296f8c8691c44f96964b1272ec3b2781996a93d1353ffbf4ab51dfe","abstract_canon_sha256":"e2888f2d282a791f03efd4311f8996abc6d921bbbb8c29bd5ef670057865459a"},"schema_version":"1.0"},"canonical_sha256":"92920c9ec6bef3bc6852094dc4f935352d1b9cbd560fdee20a0000cb88238788","source":{"kind":"arxiv","id":"1506.06285","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.06285","created_at":"2026-05-18T01:41:45Z"},{"alias_kind":"arxiv_version","alias_value":"1506.06285v1","created_at":"2026-05-18T01:41:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.06285","created_at":"2026-05-18T01:41:45Z"},{"alias_kind":"pith_short_12","alias_value":"SKJAZHWGX3Z3","created_at":"2026-05-18T12:29:42Z"},{"alias_kind":"pith_short_16","alias_value":"SKJAZHWGX3Z3Y2CS","created_at":"2026-05-18T12:29:42Z"},{"alias_kind":"pith_short_8","alias_value":"SKJAZHWG","created_at":"2026-05-18T12:29:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:SKJAZHWGX3Z3Y2CSBFG4J6JVGU","target":"record","payload":{"canonical_record":{"source":{"id":"1506.06285","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-06-20T19:25:14Z","cross_cats_sorted":[],"title_canon_sha256":"a07ab7771296f8c8691c44f96964b1272ec3b2781996a93d1353ffbf4ab51dfe","abstract_canon_sha256":"e2888f2d282a791f03efd4311f8996abc6d921bbbb8c29bd5ef670057865459a"},"schema_version":"1.0"},"canonical_sha256":"92920c9ec6bef3bc6852094dc4f935352d1b9cbd560fdee20a0000cb88238788","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:41:45.603669Z","signature_b64":"jROyXdKw4XL+dT0xOidXPKJ7y9hw8nTeBm4+nKPGZjjLwolKm1Y3av7JDqQpL0c3Myh/1f0UtG+Q7EoSRzGQAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"92920c9ec6bef3bc6852094dc4f935352d1b9cbd560fdee20a0000cb88238788","last_reissued_at":"2026-05-18T01:41:45.603197Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:41:45.603197Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1506.06285","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":"2026-05-18T01:41:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"phfiU0LrFfrscfE+IiTozsgPneOKwPcdi8JoNZLwTnj1Xsm5ce9iXhKgbsJZ4w5cvx/pEMjMt5kY8SOvxKYjAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T08:45:11.546338Z"},"content_sha256":"9276886a399f3c41019732d4f7024daf35401cec1a46b3e467fb7011b0034c41","schema_version":"1.0","event_id":"sha256:9276886a399f3c41019732d4f7024daf35401cec1a46b3e467fb7011b0034c41"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:SKJAZHWGX3Z3Y2CSBFG4J6JVGU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The MCMC split sampler: A block Gibbs sampling scheme for latent Gaussian models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Birgir Hrafnkelsson, Daniel Simpson, Helgi Sigur{\\dh}arson, \\'Oli P\\'all Geirsson","submitted_at":"2015-06-20T19:25:14Z","abstract_excerpt":"A novel computationally efficient Markov chain Monte Carlo (MCMC) scheme for latent Gaussian models (LGMs) is proposed in this paper. The sampling scheme is a two block Gibbs sampling scheme designed to exploit the model structure of LGMs. We refer to the proposed sampling scheme as the MCMC split sampler. The principle idea behind the MCMC split sampler is to split the latent Gaussian parameters into two vectors. The former vector consists of latent parameters which appear in the data density function, while the latter vector consists of latent parameters which do not appear in it. The former"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.06285","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":"2026-05-18T01:41:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ApDng+XXFFSkskxqGaXnKpWArn51t+kcKqjMzwxNcMwaA1yCCK5Fl+zxhuyHsyrsw8riKeY+qSAp44Ypq1ztBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T08:45:11.546688Z"},"content_sha256":"3250c51c16b5637ca26666d89899e3fc29048d8938c614645097e6241bc97641","schema_version":"1.0","event_id":"sha256:3250c51c16b5637ca26666d89899e3fc29048d8938c614645097e6241bc97641"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SKJAZHWGX3Z3Y2CSBFG4J6JVGU/bundle.json","state_url":"https://pith.science/pith/SKJAZHWGX3Z3Y2CSBFG4J6JVGU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SKJAZHWGX3Z3Y2CSBFG4J6JVGU/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":"2026-05-28T08:45:11Z","links":{"resolver":"https://pith.science/pith/SKJAZHWGX3Z3Y2CSBFG4J6JVGU","bundle":"https://pith.science/pith/SKJAZHWGX3Z3Y2CSBFG4J6JVGU/bundle.json","state":"https://pith.science/pith/SKJAZHWGX3Z3Y2CSBFG4J6JVGU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SKJAZHWGX3Z3Y2CSBFG4J6JVGU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:SKJAZHWGX3Z3Y2CSBFG4J6JVGU","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":"e2888f2d282a791f03efd4311f8996abc6d921bbbb8c29bd5ef670057865459a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-06-20T19:25:14Z","title_canon_sha256":"a07ab7771296f8c8691c44f96964b1272ec3b2781996a93d1353ffbf4ab51dfe"},"schema_version":"1.0","source":{"id":"1506.06285","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.06285","created_at":"2026-05-18T01:41:45Z"},{"alias_kind":"arxiv_version","alias_value":"1506.06285v1","created_at":"2026-05-18T01:41:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.06285","created_at":"2026-05-18T01:41:45Z"},{"alias_kind":"pith_short_12","alias_value":"SKJAZHWGX3Z3","created_at":"2026-05-18T12:29:42Z"},{"alias_kind":"pith_short_16","alias_value":"SKJAZHWGX3Z3Y2CS","created_at":"2026-05-18T12:29:42Z"},{"alias_kind":"pith_short_8","alias_value":"SKJAZHWG","created_at":"2026-05-18T12:29:42Z"}],"graph_snapshots":[{"event_id":"sha256:3250c51c16b5637ca26666d89899e3fc29048d8938c614645097e6241bc97641","target":"graph","created_at":"2026-05-18T01:41:45Z","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 novel computationally efficient Markov chain Monte Carlo (MCMC) scheme for latent Gaussian models (LGMs) is proposed in this paper. The sampling scheme is a two block Gibbs sampling scheme designed to exploit the model structure of LGMs. We refer to the proposed sampling scheme as the MCMC split sampler. The principle idea behind the MCMC split sampler is to split the latent Gaussian parameters into two vectors. The former vector consists of latent parameters which appear in the data density function, while the latter vector consists of latent parameters which do not appear in it. The former","authors_text":"Birgir Hrafnkelsson, Daniel Simpson, Helgi Sigur{\\dh}arson, \\'Oli P\\'all Geirsson","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-06-20T19:25:14Z","title":"The MCMC split sampler: A block Gibbs sampling scheme for latent Gaussian models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.06285","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":"sha256:9276886a399f3c41019732d4f7024daf35401cec1a46b3e467fb7011b0034c41","target":"record","created_at":"2026-05-18T01:41:45Z","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":"e2888f2d282a791f03efd4311f8996abc6d921bbbb8c29bd5ef670057865459a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-06-20T19:25:14Z","title_canon_sha256":"a07ab7771296f8c8691c44f96964b1272ec3b2781996a93d1353ffbf4ab51dfe"},"schema_version":"1.0","source":{"id":"1506.06285","kind":"arxiv","version":1}},"canonical_sha256":"92920c9ec6bef3bc6852094dc4f935352d1b9cbd560fdee20a0000cb88238788","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"92920c9ec6bef3bc6852094dc4f935352d1b9cbd560fdee20a0000cb88238788","first_computed_at":"2026-05-18T01:41:45.603197Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:41:45.603197Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jROyXdKw4XL+dT0xOidXPKJ7y9hw8nTeBm4+nKPGZjjLwolKm1Y3av7JDqQpL0c3Myh/1f0UtG+Q7EoSRzGQAg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:41:45.603669Z","signed_message":"canonical_sha256_bytes"},"source_id":"1506.06285","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9276886a399f3c41019732d4f7024daf35401cec1a46b3e467fb7011b0034c41","sha256:3250c51c16b5637ca26666d89899e3fc29048d8938c614645097e6241bc97641"],"state_sha256":"4919acc391c3b8575540e8b04671aa3673f16b8ad78fcb0569f93a1f01d1f718"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HXndmJRAsn14iF222n8MyD1g62Enaag90N1PVJzjElWkOm+NuLuN2M00GQ77QZTp5yNueUwZ7ae1wI8xfva9Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T08:45:11.548698Z","bundle_sha256":"b5b26eda0f77d9229f6b245a26a4a32cf95ab3765d1a7d8dac7e5ae096c0bddc"}}