{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2008:27J7RWBXGJ33EATJ3KJSG4PI37","short_pith_number":"pith:27J7RWBX","schema_version":"1.0","canonical_sha256":"d7d3f8d8373277b20269da932371e8dfccde05ecf5c4033d17f3349540955b77","source":{"kind":"arxiv","id":"0805.0053","version":1},"attestation_state":"computed","paper":{"title":"Particle Filtering for Large Dimensional State Spaces with Multimodal Observation Likelihoods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT","math.ST","stat.ME","stat.TH"],"primary_cat":"cs.IT","authors_text":"Namrata Vaswani","submitted_at":"2008-05-01T05:41:09Z","abstract_excerpt":"We study efficient importance sampling techniques for particle filtering (PF) when either (a) the observation likelihood (OL) is frequently multimodal or heavy-tailed, or (b) the state space dimension is large or both. When the OL is multimodal, but the state transition pdf (STP) is narrow enough, the optimal importance density is usually unimodal. Under this assumption, many techniques have been proposed. But when the STP is broad, this assumption does not hold. We study how existing techniques can be generalized to situations where the optimal importance density is multimodal, but is unimoda"},"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":"0805.0053","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2008-05-01T05:41:09Z","cross_cats_sorted":["math.IT","math.ST","stat.ME","stat.TH"],"title_canon_sha256":"87e15e5d59886682a5a3483d7d40d28722cd208675ad14d0c39c3acfba432a8a","abstract_canon_sha256":"ee4b0524fcf10f1b747c6a9e556a5e5a1151878c1afb77e00a94cb09b14ccdcc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:24:36.425840Z","signature_b64":"XPVQHSaJRugkxIlncRB3OBgSexp9/QKpWKVBbtpf74WBRPfdbyGsyhTKnXns0NJkVdwKuKG4BMi/zqhWNsCSAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7d3f8d8373277b20269da932371e8dfccde05ecf5c4033d17f3349540955b77","last_reissued_at":"2026-05-18T04:24:36.425417Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:24:36.425417Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Particle Filtering for Large Dimensional State Spaces with Multimodal Observation Likelihoods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT","math.ST","stat.ME","stat.TH"],"primary_cat":"cs.IT","authors_text":"Namrata Vaswani","submitted_at":"2008-05-01T05:41:09Z","abstract_excerpt":"We study efficient importance sampling techniques for particle filtering (PF) when either (a) the observation likelihood (OL) is frequently multimodal or heavy-tailed, or (b) the state space dimension is large or both. When the OL is multimodal, but the state transition pdf (STP) is narrow enough, the optimal importance density is usually unimodal. Under this assumption, many techniques have been proposed. But when the STP is broad, this assumption does not hold. We study how existing techniques can be generalized to situations where the optimal importance density is multimodal, but is unimoda"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"0805.0053","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":"0805.0053","created_at":"2026-05-18T04:24:36.425482+00:00"},{"alias_kind":"arxiv_version","alias_value":"0805.0053v1","created_at":"2026-05-18T04:24:36.425482+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.0805.0053","created_at":"2026-05-18T04:24:36.425482+00:00"},{"alias_kind":"pith_short_12","alias_value":"27J7RWBXGJ33","created_at":"2026-05-18T12:25:56.245647+00:00"},{"alias_kind":"pith_short_16","alias_value":"27J7RWBXGJ33EATJ","created_at":"2026-05-18T12:25:56.245647+00:00"},{"alias_kind":"pith_short_8","alias_value":"27J7RWBX","created_at":"2026-05-18T12:25:56.245647+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/27J7RWBXGJ33EATJ3KJSG4PI37","json":"https://pith.science/pith/27J7RWBXGJ33EATJ3KJSG4PI37.json","graph_json":"https://pith.science/api/pith-number/27J7RWBXGJ33EATJ3KJSG4PI37/graph.json","events_json":"https://pith.science/api/pith-number/27J7RWBXGJ33EATJ3KJSG4PI37/events.json","paper":"https://pith.science/paper/27J7RWBX"},"agent_actions":{"view_html":"https://pith.science/pith/27J7RWBXGJ33EATJ3KJSG4PI37","download_json":"https://pith.science/pith/27J7RWBXGJ33EATJ3KJSG4PI37.json","view_paper":"https://pith.science/paper/27J7RWBX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=0805.0053&json=true","fetch_graph":"https://pith.science/api/pith-number/27J7RWBXGJ33EATJ3KJSG4PI37/graph.json","fetch_events":"https://pith.science/api/pith-number/27J7RWBXGJ33EATJ3KJSG4PI37/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/27J7RWBXGJ33EATJ3KJSG4PI37/action/timestamp_anchor","attest_storage":"https://pith.science/pith/27J7RWBXGJ33EATJ3KJSG4PI37/action/storage_attestation","attest_author":"https://pith.science/pith/27J7RWBXGJ33EATJ3KJSG4PI37/action/author_attestation","sign_citation":"https://pith.science/pith/27J7RWBXGJ33EATJ3KJSG4PI37/action/citation_signature","submit_replication":"https://pith.science/pith/27J7RWBXGJ33EATJ3KJSG4PI37/action/replication_record"}},"created_at":"2026-05-18T04:24:36.425482+00:00","updated_at":"2026-05-18T04:24:36.425482+00:00"}