{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:N75IHUV275I32ZQ24SKDETKJ2U","short_pith_number":"pith:N75IHUV2","schema_version":"1.0","canonical_sha256":"6ffa83d2baff51bd661ae494324d49d5224abad8ad845ac07b13849600ed15e4","source":{"kind":"arxiv","id":"1210.0333","version":2},"attestation_state":"computed","paper":{"title":"Bayesian computing with INLA: new features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Daniel Simpson, Finn Lindgren, H{\\aa}vard Rue, Thiago G. Martins","submitted_at":"2012-10-01T10:06:54Z","abstract_excerpt":"The INLA approach for approximate Bayesian inference for latent Gaussian models has been shown to give fast and accurate estimates of posterior marginals and also to be a valuable tool in practice via the R-package R-INLA. In this paper we formalize new developments in the R-INLA package and show how these features greatly extend the scope of models that can be analyzed by this interface. We also discuss the current default method in R-INLA to approximate posterior marginals of the hyperparameters using only a modest number of evaluations of the joint posterior distribution of the hyperparamet"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1210.0333","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2012-10-01T10:06:54Z","cross_cats_sorted":[],"title_canon_sha256":"d83251e3e052077310f23cce388ebdf3b691e6a6f7724ad518b3633c6b6822b7","abstract_canon_sha256":"6864096f0f2498200fbd127b44f9b9b9c598d506e9041ec817f86d32cd3c3c48"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:33:11.548040Z","signature_b64":"U0Xqz+zJHQ2zcJgzTM46x3+lNyKZBESSTxOpyqhWy/me9yrDtB/CbZUCZmm9ZqS2W8Ty444RBsLqgtD+wwUdAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ffa83d2baff51bd661ae494324d49d5224abad8ad845ac07b13849600ed15e4","last_reissued_at":"2026-05-18T03:33:11.547330Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:33:11.547330Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian computing with INLA: new features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Daniel Simpson, Finn Lindgren, H{\\aa}vard Rue, Thiago G. Martins","submitted_at":"2012-10-01T10:06:54Z","abstract_excerpt":"The INLA approach for approximate Bayesian inference for latent Gaussian models has been shown to give fast and accurate estimates of posterior marginals and also to be a valuable tool in practice via the R-package R-INLA. In this paper we formalize new developments in the R-INLA package and show how these features greatly extend the scope of models that can be analyzed by this interface. We also discuss the current default method in R-INLA to approximate posterior marginals of the hyperparameters using only a modest number of evaluations of the joint posterior distribution of the hyperparamet"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1210.0333","kind":"arxiv","version":2},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1210.0333","created_at":"2026-05-18T03:33:11.547439+00:00"},{"alias_kind":"arxiv_version","alias_value":"1210.0333v2","created_at":"2026-05-18T03:33:11.547439+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1210.0333","created_at":"2026-05-18T03:33:11.547439+00:00"},{"alias_kind":"pith_short_12","alias_value":"N75IHUV275I3","created_at":"2026-05-18T12:27:16.716162+00:00"},{"alias_kind":"pith_short_16","alias_value":"N75IHUV275I32ZQ2","created_at":"2026-05-18T12:27:16.716162+00:00"},{"alias_kind":"pith_short_8","alias_value":"N75IHUV2","created_at":"2026-05-18T12:27:16.716162+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/N75IHUV275I32ZQ24SKDETKJ2U","json":"https://pith.science/pith/N75IHUV275I32ZQ24SKDETKJ2U.json","graph_json":"https://pith.science/api/pith-number/N75IHUV275I32ZQ24SKDETKJ2U/graph.json","events_json":"https://pith.science/api/pith-number/N75IHUV275I32ZQ24SKDETKJ2U/events.json","paper":"https://pith.science/paper/N75IHUV2"},"agent_actions":{"view_html":"https://pith.science/pith/N75IHUV275I32ZQ24SKDETKJ2U","download_json":"https://pith.science/pith/N75IHUV275I32ZQ24SKDETKJ2U.json","view_paper":"https://pith.science/paper/N75IHUV2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1210.0333&json=true","fetch_graph":"https://pith.science/api/pith-number/N75IHUV275I32ZQ24SKDETKJ2U/graph.json","fetch_events":"https://pith.science/api/pith-number/N75IHUV275I32ZQ24SKDETKJ2U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N75IHUV275I32ZQ24SKDETKJ2U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N75IHUV275I32ZQ24SKDETKJ2U/action/storage_attestation","attest_author":"https://pith.science/pith/N75IHUV275I32ZQ24SKDETKJ2U/action/author_attestation","sign_citation":"https://pith.science/pith/N75IHUV275I32ZQ24SKDETKJ2U/action/citation_signature","submit_replication":"https://pith.science/pith/N75IHUV275I32ZQ24SKDETKJ2U/action/replication_record"}},"created_at":"2026-05-18T03:33:11.547439+00:00","updated_at":"2026-05-18T03:33:11.547439+00:00"}