{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:ACJ6A6EYE7KNIS6QJC4G5ZA2AT","short_pith_number":"pith:ACJ6A6EY","schema_version":"1.0","canonical_sha256":"0093e0789827d4d44bd048b86ee41a04d31ce8141b12296cfbb3064c62d7124e","source":{"kind":"arxiv","id":"1505.04724","version":1},"attestation_state":"computed","paper":{"title":"A Hybrid Monte-Carlo Sampling Smoother for Four Dimensional Data Assimilation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"cs.NA","authors_text":"Adrian Sandu, Ahmed Attia, Vishwas Rao","submitted_at":"2015-05-18T17:15:49Z","abstract_excerpt":"This paper constructs an ensemble-based sampling smoother for four-dimensional data assimilation using a Hybrid/Hamiltonian Monte-Carlo approach. The smoother samples efficiently from the posterior probability density of the solution at the initial time. Unlike the well-known ensemble Kalman smoother, which is optimal only in the linear Gaussian case, the proposed methodology naturally accommodates non-Gaussian errors and non-linear model dynamics and observation operators. Unlike the four-dimensional variational met\\-hod, which only finds a mode of the posterior distribution, the smoother pro"},"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":"1505.04724","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NA","submitted_at":"2015-05-18T17:15:49Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"3ee9565ef5221600a4854fa862feb0442e284bbd45bd69cda4d4150dfbc79852","abstract_canon_sha256":"bb061b3979a450d551bdbf1496f1f6ed9d15f8d53e27c94db198a57aedab001b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:07:24.784828Z","signature_b64":"oaisjRu7QDc+5u3RSTfhgSe1wHrtRP0RX4LbA5rj7hWggSNdqWX/z+7RVxGrcBM7J+kbNRVuM4Y71xM+z5OMAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0093e0789827d4d44bd048b86ee41a04d31ce8141b12296cfbb3064c62d7124e","last_reissued_at":"2026-05-18T02:07:24.784355Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:07:24.784355Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Hybrid Monte-Carlo Sampling Smoother for Four Dimensional Data Assimilation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"cs.NA","authors_text":"Adrian Sandu, Ahmed Attia, Vishwas Rao","submitted_at":"2015-05-18T17:15:49Z","abstract_excerpt":"This paper constructs an ensemble-based sampling smoother for four-dimensional data assimilation using a Hybrid/Hamiltonian Monte-Carlo approach. The smoother samples efficiently from the posterior probability density of the solution at the initial time. Unlike the well-known ensemble Kalman smoother, which is optimal only in the linear Gaussian case, the proposed methodology naturally accommodates non-Gaussian errors and non-linear model dynamics and observation operators. Unlike the four-dimensional variational met\\-hod, which only finds a mode of the posterior distribution, the smoother pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1505.04724","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1505.04724","created_at":"2026-05-18T02:07:24.784428+00:00"},{"alias_kind":"arxiv_version","alias_value":"1505.04724v1","created_at":"2026-05-18T02:07:24.784428+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1505.04724","created_at":"2026-05-18T02:07:24.784428+00:00"},{"alias_kind":"pith_short_12","alias_value":"ACJ6A6EYE7KN","created_at":"2026-05-18T12:29:10.953037+00:00"},{"alias_kind":"pith_short_16","alias_value":"ACJ6A6EYE7KNIS6Q","created_at":"2026-05-18T12:29:10.953037+00:00"},{"alias_kind":"pith_short_8","alias_value":"ACJ6A6EY","created_at":"2026-05-18T12:29:10.953037+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/ACJ6A6EYE7KNIS6QJC4G5ZA2AT","json":"https://pith.science/pith/ACJ6A6EYE7KNIS6QJC4G5ZA2AT.json","graph_json":"https://pith.science/api/pith-number/ACJ6A6EYE7KNIS6QJC4G5ZA2AT/graph.json","events_json":"https://pith.science/api/pith-number/ACJ6A6EYE7KNIS6QJC4G5ZA2AT/events.json","paper":"https://pith.science/paper/ACJ6A6EY"},"agent_actions":{"view_html":"https://pith.science/pith/ACJ6A6EYE7KNIS6QJC4G5ZA2AT","download_json":"https://pith.science/pith/ACJ6A6EYE7KNIS6QJC4G5ZA2AT.json","view_paper":"https://pith.science/paper/ACJ6A6EY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1505.04724&json=true","fetch_graph":"https://pith.science/api/pith-number/ACJ6A6EYE7KNIS6QJC4G5ZA2AT/graph.json","fetch_events":"https://pith.science/api/pith-number/ACJ6A6EYE7KNIS6QJC4G5ZA2AT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ACJ6A6EYE7KNIS6QJC4G5ZA2AT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ACJ6A6EYE7KNIS6QJC4G5ZA2AT/action/storage_attestation","attest_author":"https://pith.science/pith/ACJ6A6EYE7KNIS6QJC4G5ZA2AT/action/author_attestation","sign_citation":"https://pith.science/pith/ACJ6A6EYE7KNIS6QJC4G5ZA2AT/action/citation_signature","submit_replication":"https://pith.science/pith/ACJ6A6EYE7KNIS6QJC4G5ZA2AT/action/replication_record"}},"created_at":"2026-05-18T02:07:24.784428+00:00","updated_at":"2026-05-18T02:07:24.784428+00:00"}