{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:W46KVSNAFMX6OIBZ5RICTDIKWM","short_pith_number":"pith:W46KVSNA","schema_version":"1.0","canonical_sha256":"b73caac9a02b2fe72039ec50298d0ab3187be7fcad250fda80f65309354751fa","source":{"kind":"arxiv","id":"1501.01202","version":2},"attestation_state":"computed","paper":{"title":"On Probability Estimation by Exponential Smoothing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Christopher Mattern","submitted_at":"2015-01-06T15:31:53Z","abstract_excerpt":"Probability estimation is essential for every statistical data compression algorithm. In practice probability estimation should be adaptive, recent observations should receive a higher weight than older observations. We present a probability estimation method based on exponential smoothing that satisfies this requirement and runs in constant time per letter. Our main contribution is a theoretical analysis in case of a binary alphabet for various smoothing rate sequences: We show that the redundancy w.r.t. a piecewise stationary model with $s$ segments is $O\\left(s\\sqrt n\\right)$ for any bit se"},"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":"1501.01202","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2015-01-06T15:31:53Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"3ca93a30dca3745153bb6f489402865eef15a929b5fd31517788d29a3387e454","abstract_canon_sha256":"c5471251bc3ee3ec9c1ae6adff6577db5d0e2033f7bf404303795554fbe4aed0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:29:45.151056Z","signature_b64":"UBAb9ch4hZpueEogi0jK3Gta86PFSWARHYIUsmh3dJWMp63to6hg0jUaPfAUQisFQ7qYHIRggqXmdoe4BHr0DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b73caac9a02b2fe72039ec50298d0ab3187be7fcad250fda80f65309354751fa","last_reissued_at":"2026-05-18T02:29:45.150688Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:29:45.150688Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On Probability Estimation by Exponential Smoothing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Christopher Mattern","submitted_at":"2015-01-06T15:31:53Z","abstract_excerpt":"Probability estimation is essential for every statistical data compression algorithm. In practice probability estimation should be adaptive, recent observations should receive a higher weight than older observations. We present a probability estimation method based on exponential smoothing that satisfies this requirement and runs in constant time per letter. Our main contribution is a theoretical analysis in case of a binary alphabet for various smoothing rate sequences: We show that the redundancy w.r.t. a piecewise stationary model with $s$ segments is $O\\left(s\\sqrt n\\right)$ for any bit se"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1501.01202","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":""},"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":"1501.01202","created_at":"2026-05-18T02:29:45.150745+00:00"},{"alias_kind":"arxiv_version","alias_value":"1501.01202v2","created_at":"2026-05-18T02:29:45.150745+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1501.01202","created_at":"2026-05-18T02:29:45.150745+00:00"},{"alias_kind":"pith_short_12","alias_value":"W46KVSNAFMX6","created_at":"2026-05-18T12:29:47.479230+00:00"},{"alias_kind":"pith_short_16","alias_value":"W46KVSNAFMX6OIBZ","created_at":"2026-05-18T12:29:47.479230+00:00"},{"alias_kind":"pith_short_8","alias_value":"W46KVSNA","created_at":"2026-05-18T12:29:47.479230+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/W46KVSNAFMX6OIBZ5RICTDIKWM","json":"https://pith.science/pith/W46KVSNAFMX6OIBZ5RICTDIKWM.json","graph_json":"https://pith.science/api/pith-number/W46KVSNAFMX6OIBZ5RICTDIKWM/graph.json","events_json":"https://pith.science/api/pith-number/W46KVSNAFMX6OIBZ5RICTDIKWM/events.json","paper":"https://pith.science/paper/W46KVSNA"},"agent_actions":{"view_html":"https://pith.science/pith/W46KVSNAFMX6OIBZ5RICTDIKWM","download_json":"https://pith.science/pith/W46KVSNAFMX6OIBZ5RICTDIKWM.json","view_paper":"https://pith.science/paper/W46KVSNA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1501.01202&json=true","fetch_graph":"https://pith.science/api/pith-number/W46KVSNAFMX6OIBZ5RICTDIKWM/graph.json","fetch_events":"https://pith.science/api/pith-number/W46KVSNAFMX6OIBZ5RICTDIKWM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W46KVSNAFMX6OIBZ5RICTDIKWM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W46KVSNAFMX6OIBZ5RICTDIKWM/action/storage_attestation","attest_author":"https://pith.science/pith/W46KVSNAFMX6OIBZ5RICTDIKWM/action/author_attestation","sign_citation":"https://pith.science/pith/W46KVSNAFMX6OIBZ5RICTDIKWM/action/citation_signature","submit_replication":"https://pith.science/pith/W46KVSNAFMX6OIBZ5RICTDIKWM/action/replication_record"}},"created_at":"2026-05-18T02:29:45.150745+00:00","updated_at":"2026-05-18T02:29:45.150745+00:00"}