{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:64MQG4AQP3CXB7ISTA6FRLIUSV","short_pith_number":"pith:64MQG4AQ","schema_version":"1.0","canonical_sha256":"f7190370107ec570fd12983c58ad1495580e35acd67b056f7c04cef23d1ba849","source":{"kind":"arxiv","id":"1302.6563","version":1},"attestation_state":"computed","paper":{"title":"Feedback Particle Filter","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.NA","authors_text":"Prashant G. Mehta, Sean P. Meyn, Tao Yang","submitted_at":"2013-02-26T20:13:54Z","abstract_excerpt":"A new formulation of the particle filter for nonlinear filtering is presented, based on concepts from optimal control, and from the mean-field game theory. The optimal control is chosen so that the posterior distribution of a particle matches as closely as possible the posterior distribution of the true state given the observations. This is achieved by introducing a cost function, defined by the Kullback-Leibler (K-L) divergence between the actual posterior, and the posterior of any particle.\n  The optimal control input is characterized by a certain Euler-Lagrange (E-L) equation, and is shown "},"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":"1302.6563","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2013-02-26T20:13:54Z","cross_cats_sorted":[],"title_canon_sha256":"0f6022d1c2d68112b6cf690f2012d69bdeb11536a43f635d148309db8a912b11","abstract_canon_sha256":"6a1ee7ed6b1386654c70ad3a603c9892fe5c188b5a626840df7ac77f210d9719"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:32:28.929944Z","signature_b64":"Nk/V0pSmHdqZrTeIaZLDWzOhcyJloYdWmoYKzWxUrE0FYVUBHUazUJEMDN5KDDFTB2apmeE+XCDV6+EmAe5FAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f7190370107ec570fd12983c58ad1495580e35acd67b056f7c04cef23d1ba849","last_reissued_at":"2026-05-18T03:32:28.929183Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:32:28.929183Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Feedback Particle Filter","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.NA","authors_text":"Prashant G. Mehta, Sean P. Meyn, Tao Yang","submitted_at":"2013-02-26T20:13:54Z","abstract_excerpt":"A new formulation of the particle filter for nonlinear filtering is presented, based on concepts from optimal control, and from the mean-field game theory. The optimal control is chosen so that the posterior distribution of a particle matches as closely as possible the posterior distribution of the true state given the observations. This is achieved by introducing a cost function, defined by the Kullback-Leibler (K-L) divergence between the actual posterior, and the posterior of any particle.\n  The optimal control input is characterized by a certain Euler-Lagrange (E-L) equation, and is shown "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1302.6563","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":"1302.6563","created_at":"2026-05-18T03:32:28.929310+00:00"},{"alias_kind":"arxiv_version","alias_value":"1302.6563v1","created_at":"2026-05-18T03:32:28.929310+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1302.6563","created_at":"2026-05-18T03:32:28.929310+00:00"},{"alias_kind":"pith_short_12","alias_value":"64MQG4AQP3CX","created_at":"2026-05-18T12:27:34.582898+00:00"},{"alias_kind":"pith_short_16","alias_value":"64MQG4AQP3CXB7IS","created_at":"2026-05-18T12:27:34.582898+00:00"},{"alias_kind":"pith_short_8","alias_value":"64MQG4AQ","created_at":"2026-05-18T12:27:34.582898+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/64MQG4AQP3CXB7ISTA6FRLIUSV","json":"https://pith.science/pith/64MQG4AQP3CXB7ISTA6FRLIUSV.json","graph_json":"https://pith.science/api/pith-number/64MQG4AQP3CXB7ISTA6FRLIUSV/graph.json","events_json":"https://pith.science/api/pith-number/64MQG4AQP3CXB7ISTA6FRLIUSV/events.json","paper":"https://pith.science/paper/64MQG4AQ"},"agent_actions":{"view_html":"https://pith.science/pith/64MQG4AQP3CXB7ISTA6FRLIUSV","download_json":"https://pith.science/pith/64MQG4AQP3CXB7ISTA6FRLIUSV.json","view_paper":"https://pith.science/paper/64MQG4AQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1302.6563&json=true","fetch_graph":"https://pith.science/api/pith-number/64MQG4AQP3CXB7ISTA6FRLIUSV/graph.json","fetch_events":"https://pith.science/api/pith-number/64MQG4AQP3CXB7ISTA6FRLIUSV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/64MQG4AQP3CXB7ISTA6FRLIUSV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/64MQG4AQP3CXB7ISTA6FRLIUSV/action/storage_attestation","attest_author":"https://pith.science/pith/64MQG4AQP3CXB7ISTA6FRLIUSV/action/author_attestation","sign_citation":"https://pith.science/pith/64MQG4AQP3CXB7ISTA6FRLIUSV/action/citation_signature","submit_replication":"https://pith.science/pith/64MQG4AQP3CXB7ISTA6FRLIUSV/action/replication_record"}},"created_at":"2026-05-18T03:32:28.929310+00:00","updated_at":"2026-05-18T03:32:28.929310+00:00"}