{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:OQIK4LVIPJLF4ZH5HDZED4SQAM","short_pith_number":"pith:OQIK4LVI","schema_version":"1.0","canonical_sha256":"7410ae2ea87a565e64fd38f241f250031f64724e3bf9e6f7aeb01bb68a74c738","source":{"kind":"arxiv","id":"1812.05325","version":1},"attestation_state":"computed","paper":{"title":"Probing high order dependencies with information theory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.data-an","authors_text":"C Granero-Belinch\\'on (ONERA / DOTA, France, INRA), N. Garnier (Phys-ENS), P. Abry (Phys-ENS), S. Roux (Phys-ENS), Universit\\'e de Toulouse","submitted_at":"2018-12-13T09:20:24Z","abstract_excerpt":"Information theoretic measures (entropies, entropy rates, mutual information) are nowadays commonly used in statistical signal processing for real-world data analysis. The present work proposes the use of Auto Mutual Information (Mutual Information between subsets of the same signal) and entropy rate as powerful tools to assess refined dependencies of any order in signal temporal dynamics. Notably, it is shown how two-point Auto Mutual Information and entropy rate unveil information conveyed by higher order statistic and thus capture details of temporal dynamics that are overlooked by the (two"},"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":"1812.05325","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2018-12-13T09:20:24Z","cross_cats_sorted":[],"title_canon_sha256":"be0737462235ec1611488f2718ef4d118b13f140ad225c1f3591d33d7476827b","abstract_canon_sha256":"a62cb4693d3bbad4298063560d9358e864d8cb55e45169c06241d9f4b169b086"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:57.626293Z","signature_b64":"gkSc0Wqzpdy+DchRxVtVeMMkgr9DLnbttXV9duKcjea0JpYy/tUjuMVupGfYx2/9GYtex2nlahlhbikJHe60DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7410ae2ea87a565e64fd38f241f250031f64724e3bf9e6f7aeb01bb68a74c738","last_reissued_at":"2026-05-17T23:39:57.625784Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:57.625784Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Probing high order dependencies with information theory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.data-an","authors_text":"C Granero-Belinch\\'on (ONERA / DOTA, France, INRA), N. Garnier (Phys-ENS), P. Abry (Phys-ENS), S. Roux (Phys-ENS), Universit\\'e de Toulouse","submitted_at":"2018-12-13T09:20:24Z","abstract_excerpt":"Information theoretic measures (entropies, entropy rates, mutual information) are nowadays commonly used in statistical signal processing for real-world data analysis. The present work proposes the use of Auto Mutual Information (Mutual Information between subsets of the same signal) and entropy rate as powerful tools to assess refined dependencies of any order in signal temporal dynamics. Notably, it is shown how two-point Auto Mutual Information and entropy rate unveil information conveyed by higher order statistic and thus capture details of temporal dynamics that are overlooked by the (two"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.05325","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":"1812.05325","created_at":"2026-05-17T23:39:57.625889+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.05325v1","created_at":"2026-05-17T23:39:57.625889+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.05325","created_at":"2026-05-17T23:39:57.625889+00:00"},{"alias_kind":"pith_short_12","alias_value":"OQIK4LVIPJLF","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_16","alias_value":"OQIK4LVIPJLF4ZH5","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_8","alias_value":"OQIK4LVI","created_at":"2026-05-18T12:32:43.782077+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/OQIK4LVIPJLF4ZH5HDZED4SQAM","json":"https://pith.science/pith/OQIK4LVIPJLF4ZH5HDZED4SQAM.json","graph_json":"https://pith.science/api/pith-number/OQIK4LVIPJLF4ZH5HDZED4SQAM/graph.json","events_json":"https://pith.science/api/pith-number/OQIK4LVIPJLF4ZH5HDZED4SQAM/events.json","paper":"https://pith.science/paper/OQIK4LVI"},"agent_actions":{"view_html":"https://pith.science/pith/OQIK4LVIPJLF4ZH5HDZED4SQAM","download_json":"https://pith.science/pith/OQIK4LVIPJLF4ZH5HDZED4SQAM.json","view_paper":"https://pith.science/paper/OQIK4LVI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.05325&json=true","fetch_graph":"https://pith.science/api/pith-number/OQIK4LVIPJLF4ZH5HDZED4SQAM/graph.json","fetch_events":"https://pith.science/api/pith-number/OQIK4LVIPJLF4ZH5HDZED4SQAM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OQIK4LVIPJLF4ZH5HDZED4SQAM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OQIK4LVIPJLF4ZH5HDZED4SQAM/action/storage_attestation","attest_author":"https://pith.science/pith/OQIK4LVIPJLF4ZH5HDZED4SQAM/action/author_attestation","sign_citation":"https://pith.science/pith/OQIK4LVIPJLF4ZH5HDZED4SQAM/action/citation_signature","submit_replication":"https://pith.science/pith/OQIK4LVIPJLF4ZH5HDZED4SQAM/action/replication_record"}},"created_at":"2026-05-17T23:39:57.625889+00:00","updated_at":"2026-05-17T23:39:57.625889+00:00"}