{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5IT4ASLJWMLL6BIBA5WBFZM5QD","short_pith_number":"pith:5IT4ASLJ","schema_version":"1.0","canonical_sha256":"ea27c04969b316bf0501076c12e59d80d5ec33b7eb6a48b5c31778ff6e51eb56","source":{"kind":"arxiv","id":"2405.05646","version":2},"attestation_state":"computed","paper":{"title":"Outlier-robust Kalman Filtering through Generalised Bayes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SY","eess.SY"],"primary_cat":"stat.ML","authors_text":"Alexander Y. Shestopaloff, Fran\\c{c}ois-Xavier Briol, Gerardo Duran-Martin, Jeremias Knoblauch, Kevin Murphy, Leandro S\\'anchez-Betancourt, Matias Altamirano, Matt Jones","submitted_at":"2024-05-09T09:40:56Z","abstract_excerpt":"We derive a novel, provably robust, and closed-form Bayesian update rule for online filtering in state-space models in the presence of outliers and misspecified measurement models. Our method combines generalised Bayesian inference with filtering methods such as the extended and ensemble Kalman filter. We use the former to show robustness and the latter to ensure computational efficiency in the case of nonlinear models. Our method matches or outperforms other robust filtering methods (such as those based on variational Bayes) at a much lower computational cost. We show this empirically on a ra"},"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":"2405.05646","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-05-09T09:40:56Z","cross_cats_sorted":["cs.LG","cs.SY","eess.SY"],"title_canon_sha256":"f62c7c0f159d9ee3941d145cb9e5593f1f98b71c88f43a05360c51ea2060cf63","abstract_canon_sha256":"7df2f1cd4ca1db2963ef693a500b0ed8c942adc88e214a8b463146b9b9050987"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:24:05.268247Z","signature_b64":"i56mlJP3Ra6iQuEuBUrJOfYApwuGltlaWPMFcavLC28e8SmpVqhkH0ghBXkfIVZd1EXrr4waoIgyoaR+YCrZBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea27c04969b316bf0501076c12e59d80d5ec33b7eb6a48b5c31778ff6e51eb56","last_reissued_at":"2026-07-05T08:24:05.267751Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:24:05.267751Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Outlier-robust Kalman Filtering through Generalised Bayes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SY","eess.SY"],"primary_cat":"stat.ML","authors_text":"Alexander Y. Shestopaloff, Fran\\c{c}ois-Xavier Briol, Gerardo Duran-Martin, Jeremias Knoblauch, Kevin Murphy, Leandro S\\'anchez-Betancourt, Matias Altamirano, Matt Jones","submitted_at":"2024-05-09T09:40:56Z","abstract_excerpt":"We derive a novel, provably robust, and closed-form Bayesian update rule for online filtering in state-space models in the presence of outliers and misspecified measurement models. Our method combines generalised Bayesian inference with filtering methods such as the extended and ensemble Kalman filter. We use the former to show robustness and the latter to ensure computational efficiency in the case of nonlinear models. Our method matches or outperforms other robust filtering methods (such as those based on variational Bayes) at a much lower computational cost. We show this empirically on a ra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.05646","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2405.05646/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2405.05646","created_at":"2026-07-05T08:24:05.267816+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.05646v2","created_at":"2026-07-05T08:24:05.267816+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.05646","created_at":"2026-07-05T08:24:05.267816+00:00"},{"alias_kind":"pith_short_12","alias_value":"5IT4ASLJWMLL","created_at":"2026-07-05T08:24:05.267816+00:00"},{"alias_kind":"pith_short_16","alias_value":"5IT4ASLJWMLL6BIB","created_at":"2026-07-05T08:24:05.267816+00:00"},{"alias_kind":"pith_short_8","alias_value":"5IT4ASLJ","created_at":"2026-07-05T08:24:05.267816+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.26881","citing_title":"Robust ensemble Kalman filtering under observation noise misspecification via diffusion score matching","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5IT4ASLJWMLL6BIBA5WBFZM5QD","json":"https://pith.science/pith/5IT4ASLJWMLL6BIBA5WBFZM5QD.json","graph_json":"https://pith.science/api/pith-number/5IT4ASLJWMLL6BIBA5WBFZM5QD/graph.json","events_json":"https://pith.science/api/pith-number/5IT4ASLJWMLL6BIBA5WBFZM5QD/events.json","paper":"https://pith.science/paper/5IT4ASLJ"},"agent_actions":{"view_html":"https://pith.science/pith/5IT4ASLJWMLL6BIBA5WBFZM5QD","download_json":"https://pith.science/pith/5IT4ASLJWMLL6BIBA5WBFZM5QD.json","view_paper":"https://pith.science/paper/5IT4ASLJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.05646&json=true","fetch_graph":"https://pith.science/api/pith-number/5IT4ASLJWMLL6BIBA5WBFZM5QD/graph.json","fetch_events":"https://pith.science/api/pith-number/5IT4ASLJWMLL6BIBA5WBFZM5QD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5IT4ASLJWMLL6BIBA5WBFZM5QD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5IT4ASLJWMLL6BIBA5WBFZM5QD/action/storage_attestation","attest_author":"https://pith.science/pith/5IT4ASLJWMLL6BIBA5WBFZM5QD/action/author_attestation","sign_citation":"https://pith.science/pith/5IT4ASLJWMLL6BIBA5WBFZM5QD/action/citation_signature","submit_replication":"https://pith.science/pith/5IT4ASLJWMLL6BIBA5WBFZM5QD/action/replication_record"}},"created_at":"2026-07-05T08:24:05.267816+00:00","updated_at":"2026-07-05T08:24:05.267816+00:00"}