{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:GMJOF53P54GTLAON3F67622AYZ","short_pith_number":"pith:GMJOF53P","schema_version":"1.0","canonical_sha256":"3312e2f76fef0d3581cdd97dff6b40c67b6c087eee8f98fa42d0164ed341f4a7","source":{"kind":"arxiv","id":"1405.4175","version":1},"attestation_state":"computed","paper":{"title":"Nonparametric Markovian Learning of Triggering Kernels for Mutually Exciting and Mutually Inhibiting Multivariate Hawkes Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.PR","authors_text":"Nicolas Vayatis, Remi Lemonnier","submitted_at":"2014-05-16T14:20:22Z","abstract_excerpt":"In this paper, we address the problem of fitting multivariate Hawkes processes to potentially large-scale data in a setting where series of events are not only mutually-exciting but can also exhibit inhibitive patterns. We focus on nonparametric learning and propose a novel algorithm called MEMIP (Markovian Estimation of Mutually Interacting Processes) that makes use of polynomial approximation theory and self-concordant analysis in order to learn both triggering kernels and base intensities of events. Moreover, considering that N historical observations are available, the algorithm performs l"},"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":"1405.4175","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.PR","submitted_at":"2014-05-16T14:20:22Z","cross_cats_sorted":[],"title_canon_sha256":"29d612691f8d70666af296160ebd297bb897e3e6538685c8eca928aa287c6dc8","abstract_canon_sha256":"65a2b6b13c20978e017dd92305c692855f078c52323b80e88eeac5461cda0f73"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:51:42.976789Z","signature_b64":"uir4FQFKHyCC6LY4WqnZYjjTTJ7jN2FIbJReIpxxN2WjtcfxZ2mA8rp4ud3FlVkbhdmlBG11mCanSnCiU5DUBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3312e2f76fef0d3581cdd97dff6b40c67b6c087eee8f98fa42d0164ed341f4a7","last_reissued_at":"2026-05-18T02:51:42.976311Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:51:42.976311Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nonparametric Markovian Learning of Triggering Kernels for Mutually Exciting and Mutually Inhibiting Multivariate Hawkes Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.PR","authors_text":"Nicolas Vayatis, Remi Lemonnier","submitted_at":"2014-05-16T14:20:22Z","abstract_excerpt":"In this paper, we address the problem of fitting multivariate Hawkes processes to potentially large-scale data in a setting where series of events are not only mutually-exciting but can also exhibit inhibitive patterns. We focus on nonparametric learning and propose a novel algorithm called MEMIP (Markovian Estimation of Mutually Interacting Processes) that makes use of polynomial approximation theory and self-concordant analysis in order to learn both triggering kernels and base intensities of events. Moreover, considering that N historical observations are available, the algorithm performs l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1405.4175","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":"1405.4175","created_at":"2026-05-18T02:51:42.976391+00:00"},{"alias_kind":"arxiv_version","alias_value":"1405.4175v1","created_at":"2026-05-18T02:51:42.976391+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1405.4175","created_at":"2026-05-18T02:51:42.976391+00:00"},{"alias_kind":"pith_short_12","alias_value":"GMJOF53P54GT","created_at":"2026-05-18T12:28:30.664211+00:00"},{"alias_kind":"pith_short_16","alias_value":"GMJOF53P54GTLAON","created_at":"2026-05-18T12:28:30.664211+00:00"},{"alias_kind":"pith_short_8","alias_value":"GMJOF53P","created_at":"2026-05-18T12:28:30.664211+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/GMJOF53P54GTLAON3F67622AYZ","json":"https://pith.science/pith/GMJOF53P54GTLAON3F67622AYZ.json","graph_json":"https://pith.science/api/pith-number/GMJOF53P54GTLAON3F67622AYZ/graph.json","events_json":"https://pith.science/api/pith-number/GMJOF53P54GTLAON3F67622AYZ/events.json","paper":"https://pith.science/paper/GMJOF53P"},"agent_actions":{"view_html":"https://pith.science/pith/GMJOF53P54GTLAON3F67622AYZ","download_json":"https://pith.science/pith/GMJOF53P54GTLAON3F67622AYZ.json","view_paper":"https://pith.science/paper/GMJOF53P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1405.4175&json=true","fetch_graph":"https://pith.science/api/pith-number/GMJOF53P54GTLAON3F67622AYZ/graph.json","fetch_events":"https://pith.science/api/pith-number/GMJOF53P54GTLAON3F67622AYZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GMJOF53P54GTLAON3F67622AYZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GMJOF53P54GTLAON3F67622AYZ/action/storage_attestation","attest_author":"https://pith.science/pith/GMJOF53P54GTLAON3F67622AYZ/action/author_attestation","sign_citation":"https://pith.science/pith/GMJOF53P54GTLAON3F67622AYZ/action/citation_signature","submit_replication":"https://pith.science/pith/GMJOF53P54GTLAON3F67622AYZ/action/replication_record"}},"created_at":"2026-05-18T02:51:42.976391+00:00","updated_at":"2026-05-18T02:51:42.976391+00:00"}