{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:GOWZ64JC5OESDKHIPBEGVWTK6E","short_pith_number":"pith:GOWZ64JC","schema_version":"1.0","canonical_sha256":"33ad9f7122eb8921a8e878486ada6af138bb7756b6dbe11746b7ccf3bbf2bebe","source":{"kind":"arxiv","id":"2307.15764","version":4},"attestation_state":"computed","paper":{"title":"Unique Ergodicity of Non-Linear Filters via Reachability and Uniform Weak Continuity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.PR","authors_text":"Serdar Y\\\"uksel, Yunus Emre Demirci","submitted_at":"2023-07-28T18:42:48Z","abstract_excerpt":"We present a reachability based approach to establish unique ergodicity of non-linear filter processes where state space of a hidden Markov model is a compact Polish metric space and the observation space is a Polish metric space. We also establish a weak convergence result on occupation measures under such a reachability condition. Our conditions, which are explicit, are complementary to those based on filter stability as demonstrated in examples."},"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":"2307.15764","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.PR","submitted_at":"2023-07-28T18:42:48Z","cross_cats_sorted":[],"title_canon_sha256":"bd4e6280c81b636fffa21c5401c6338fe0c889ad428225bf851d7dd17d525ad8","abstract_canon_sha256":"8937cf966cc6cabbf83722caa45c651b1d18a673915bd6f6881c64e050c8f3ad"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:11:11.693082Z","signature_b64":"p26MEQQhQ0tHcWc3rEELKSZZ7SPg/R2CD7NkXTBCG4Wh1NO3qjHg3G1gTZ2ooodlr/FSOzMoRbw6hcPliZ5VBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33ad9f7122eb8921a8e878486ada6af138bb7756b6dbe11746b7ccf3bbf2bebe","last_reissued_at":"2026-07-05T12:11:11.692434Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:11:11.692434Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unique Ergodicity of Non-Linear Filters via Reachability and Uniform Weak Continuity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.PR","authors_text":"Serdar Y\\\"uksel, Yunus Emre Demirci","submitted_at":"2023-07-28T18:42:48Z","abstract_excerpt":"We present a reachability based approach to establish unique ergodicity of non-linear filter processes where state space of a hidden Markov model is a compact Polish metric space and the observation space is a Polish metric space. We also establish a weak convergence result on occupation measures under such a reachability condition. Our conditions, which are explicit, are complementary to those based on filter stability as demonstrated in examples."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.15764","kind":"arxiv","version":4},"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/2307.15764/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":"2307.15764","created_at":"2026-07-05T12:11:11.692498+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.15764v4","created_at":"2026-07-05T12:11:11.692498+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.15764","created_at":"2026-07-05T12:11:11.692498+00:00"},{"alias_kind":"pith_short_12","alias_value":"GOWZ64JC5OES","created_at":"2026-07-05T12:11:11.692498+00:00"},{"alias_kind":"pith_short_16","alias_value":"GOWZ64JC5OESDKHI","created_at":"2026-07-05T12:11:11.692498+00:00"},{"alias_kind":"pith_short_8","alias_value":"GOWZ64JC","created_at":"2026-07-05T12:11:11.692498+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.06735","citing_title":"Partially Observed Optimal Stochastic Control: Regularity, Optimality, Approximations, and Learning","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GOWZ64JC5OESDKHIPBEGVWTK6E","json":"https://pith.science/pith/GOWZ64JC5OESDKHIPBEGVWTK6E.json","graph_json":"https://pith.science/api/pith-number/GOWZ64JC5OESDKHIPBEGVWTK6E/graph.json","events_json":"https://pith.science/api/pith-number/GOWZ64JC5OESDKHIPBEGVWTK6E/events.json","paper":"https://pith.science/paper/GOWZ64JC"},"agent_actions":{"view_html":"https://pith.science/pith/GOWZ64JC5OESDKHIPBEGVWTK6E","download_json":"https://pith.science/pith/GOWZ64JC5OESDKHIPBEGVWTK6E.json","view_paper":"https://pith.science/paper/GOWZ64JC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.15764&json=true","fetch_graph":"https://pith.science/api/pith-number/GOWZ64JC5OESDKHIPBEGVWTK6E/graph.json","fetch_events":"https://pith.science/api/pith-number/GOWZ64JC5OESDKHIPBEGVWTK6E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GOWZ64JC5OESDKHIPBEGVWTK6E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GOWZ64JC5OESDKHIPBEGVWTK6E/action/storage_attestation","attest_author":"https://pith.science/pith/GOWZ64JC5OESDKHIPBEGVWTK6E/action/author_attestation","sign_citation":"https://pith.science/pith/GOWZ64JC5OESDKHIPBEGVWTK6E/action/citation_signature","submit_replication":"https://pith.science/pith/GOWZ64JC5OESDKHIPBEGVWTK6E/action/replication_record"}},"created_at":"2026-07-05T12:11:11.692498+00:00","updated_at":"2026-07-05T12:11:11.692498+00:00"}