{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:LHJGFZTAF7UIGK5QBO6JHGCFAI","short_pith_number":"pith:LHJGFZTA","canonical_record":{"source":{"id":"1808.05465","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-08-15T08:10:30Z","cross_cats_sorted":["math.PR"],"title_canon_sha256":"667ed2e2e6ed0ed00c658f3dacd13a53dba33c6fa1e212ebbd3a2da073ebfbdf","abstract_canon_sha256":"c5817216cc13717c05d6b46ed682b682c8167ea03b575a4c5225b341bd231f90"},"schema_version":"1.0"},"canonical_sha256":"59d262e6602fe8832bb00bbc9398450208c4eff6a32f91223baf195456f5a45e","source":{"kind":"arxiv","id":"1808.05465","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.05465","created_at":"2026-05-18T00:07:56Z"},{"alias_kind":"arxiv_version","alias_value":"1808.05465v1","created_at":"2026-05-18T00:07:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.05465","created_at":"2026-05-18T00:07:56Z"},{"alias_kind":"pith_short_12","alias_value":"LHJGFZTAF7UI","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LHJGFZTAF7UIGK5Q","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LHJGFZTA","created_at":"2026-05-18T12:32:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:LHJGFZTAF7UIGK5QBO6JHGCFAI","target":"record","payload":{"canonical_record":{"source":{"id":"1808.05465","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-08-15T08:10:30Z","cross_cats_sorted":["math.PR"],"title_canon_sha256":"667ed2e2e6ed0ed00c658f3dacd13a53dba33c6fa1e212ebbd3a2da073ebfbdf","abstract_canon_sha256":"c5817216cc13717c05d6b46ed682b682c8167ea03b575a4c5225b341bd231f90"},"schema_version":"1.0"},"canonical_sha256":"59d262e6602fe8832bb00bbc9398450208c4eff6a32f91223baf195456f5a45e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:56.918795Z","signature_b64":"hQCvKfG9TAGQNW3f8t0iwzIhJ8S/BMO9mOQom2lXHbHBl60UIehMRjahoQ18w0XMm2kDK8GFvWRft8+qJjlDBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"59d262e6602fe8832bb00bbc9398450208c4eff6a32f91223baf195456f5a45e","last_reissued_at":"2026-05-18T00:07:56.918121Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:56.918121Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.05465","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":"2026-05-18T00:07:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5M5Wnxw8CFWZZxYP23BTYWTrOhpLwzg74yhAZOJCp+q9KBSnV3VAAZ55HC6aGEb5R6suIAtTRA+w7sTM6ZmyDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T18:28:52.692105Z"},"content_sha256":"4e94f13ce2596b2b9ab57da9ec63b0b418ed8b64a3be12af5ac84566f3878c2b","schema_version":"1.0","event_id":"sha256:4e94f13ce2596b2b9ab57da9ec63b0b418ed8b64a3be12af5ac84566f3878c2b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:LHJGFZTAF7UIGK5QBO6JHGCFAI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Trimmed Ensemble Kalman Filter for Nonlinear and Non-Gaussian Data Assimilation Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR"],"primary_cat":"stat.ME","authors_text":"Guang Lin, Weixuan Li, W. Steven Rosenthal","submitted_at":"2018-08-15T08:10:30Z","abstract_excerpt":"We study the ensemble Kalman filter (EnKF) algorithm for sequential data assimilation in a general situation, that is, for nonlinear forecast and measurement models with non-additive and non-Gaussian noises. Such applications traditionally force us to choose between inaccurate Gaussian assumptions that permit efficient algorithms (e.g., EnKF), or more accurate direct sampling methods which scale poorly with dimension (e.g., particle filters, or PF). We introduce a trimmed ensemble Kalman filter (TEnKF) which can interpolate between the limiting distributions of the EnKF and PF to facilitate ad"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.05465","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":"2026-05-18T00:07:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GVu3cSw77g2acpTv5DW0stdImKlXyF2ojsZf2gaDDeK2tRJwYt0Hk0uaUBdGdw5QnfBDx777hI80jzNd8LZVDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T18:28:52.692829Z"},"content_sha256":"e6719ab1671a681b41ce9ee8972b0e00daaa00c76925eeace1c25238439c6cf1","schema_version":"1.0","event_id":"sha256:e6719ab1671a681b41ce9ee8972b0e00daaa00c76925eeace1c25238439c6cf1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LHJGFZTAF7UIGK5QBO6JHGCFAI/bundle.json","state_url":"https://pith.science/pith/LHJGFZTAF7UIGK5QBO6JHGCFAI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LHJGFZTAF7UIGK5QBO6JHGCFAI/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":"2026-05-28T18:28:52Z","links":{"resolver":"https://pith.science/pith/LHJGFZTAF7UIGK5QBO6JHGCFAI","bundle":"https://pith.science/pith/LHJGFZTAF7UIGK5QBO6JHGCFAI/bundle.json","state":"https://pith.science/pith/LHJGFZTAF7UIGK5QBO6JHGCFAI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LHJGFZTAF7UIGK5QBO6JHGCFAI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:LHJGFZTAF7UIGK5QBO6JHGCFAI","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":"c5817216cc13717c05d6b46ed682b682c8167ea03b575a4c5225b341bd231f90","cross_cats_sorted":["math.PR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-08-15T08:10:30Z","title_canon_sha256":"667ed2e2e6ed0ed00c658f3dacd13a53dba33c6fa1e212ebbd3a2da073ebfbdf"},"schema_version":"1.0","source":{"id":"1808.05465","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.05465","created_at":"2026-05-18T00:07:56Z"},{"alias_kind":"arxiv_version","alias_value":"1808.05465v1","created_at":"2026-05-18T00:07:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.05465","created_at":"2026-05-18T00:07:56Z"},{"alias_kind":"pith_short_12","alias_value":"LHJGFZTAF7UI","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LHJGFZTAF7UIGK5Q","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LHJGFZTA","created_at":"2026-05-18T12:32:37Z"}],"graph_snapshots":[{"event_id":"sha256:e6719ab1671a681b41ce9ee8972b0e00daaa00c76925eeace1c25238439c6cf1","target":"graph","created_at":"2026-05-18T00:07:56Z","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":"We study the ensemble Kalman filter (EnKF) algorithm for sequential data assimilation in a general situation, that is, for nonlinear forecast and measurement models with non-additive and non-Gaussian noises. Such applications traditionally force us to choose between inaccurate Gaussian assumptions that permit efficient algorithms (e.g., EnKF), or more accurate direct sampling methods which scale poorly with dimension (e.g., particle filters, or PF). We introduce a trimmed ensemble Kalman filter (TEnKF) which can interpolate between the limiting distributions of the EnKF and PF to facilitate ad","authors_text":"Guang Lin, Weixuan Li, W. Steven Rosenthal","cross_cats":["math.PR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-08-15T08:10:30Z","title":"Trimmed Ensemble Kalman Filter for Nonlinear and Non-Gaussian Data Assimilation Problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.05465","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":"sha256:4e94f13ce2596b2b9ab57da9ec63b0b418ed8b64a3be12af5ac84566f3878c2b","target":"record","created_at":"2026-05-18T00:07:56Z","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":"c5817216cc13717c05d6b46ed682b682c8167ea03b575a4c5225b341bd231f90","cross_cats_sorted":["math.PR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-08-15T08:10:30Z","title_canon_sha256":"667ed2e2e6ed0ed00c658f3dacd13a53dba33c6fa1e212ebbd3a2da073ebfbdf"},"schema_version":"1.0","source":{"id":"1808.05465","kind":"arxiv","version":1}},"canonical_sha256":"59d262e6602fe8832bb00bbc9398450208c4eff6a32f91223baf195456f5a45e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"59d262e6602fe8832bb00bbc9398450208c4eff6a32f91223baf195456f5a45e","first_computed_at":"2026-05-18T00:07:56.918121Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:07:56.918121Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hQCvKfG9TAGQNW3f8t0iwzIhJ8S/BMO9mOQom2lXHbHBl60UIehMRjahoQ18w0XMm2kDK8GFvWRft8+qJjlDBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:07:56.918795Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.05465","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4e94f13ce2596b2b9ab57da9ec63b0b418ed8b64a3be12af5ac84566f3878c2b","sha256:e6719ab1671a681b41ce9ee8972b0e00daaa00c76925eeace1c25238439c6cf1"],"state_sha256":"1ed661c189e643bf90eb41875c123abde71fd050b77d0c1f78f41d664e8fe01e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"672DTUhbLBMrctwAsUK6CdN9F4tJ51aW4XCQlA05AvnkytFQ8pq6gYwpSqF5bSCoVeQKFfjQqdHeU90wbeeCBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T18:28:52.696576Z","bundle_sha256":"909db6eb2716b8354f340a1788b587465e02aab15890fbac0686336526acbbf9"}}