{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:YZ4VWLT6RBXWBOG5GZN6K52B6B","short_pith_number":"pith:YZ4VWLT6","schema_version":"1.0","canonical_sha256":"c6795b2e7e886f60b8dd365be57741f0625c52f96f5f1d44b06975a6959db6c7","source":{"kind":"arxiv","id":"2103.17164","version":3},"attestation_state":"computed","paper":{"title":"Bayesian estimation of nonlinear Hawkes process","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Deborah Sulem, Judith Rousseau, Vincent Rivoirard","submitted_at":"2021-03-31T15:28:51Z","abstract_excerpt":"Multivariate point processes are widely applied to model event-type data such as natural disasters, online message exchanges, financial transactions or neuronal spike trains. One very popular point process model in which the probability of occurrences of new events depend on the past of the process is the Hawkes process. In this work we consider the nonlinear Hawkes process, which notably models excitation and inhibition phenomena between dimensions of the process. In a nonparametric Bayesian estimation framework, we obtain concentration rates of the posterior distribution on the parameters, u"},"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":"2103.17164","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2021-03-31T15:28:51Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"99fba0d7ea32de6ecbf6b556b75ff1e7eb7405ae3f305622fb990ed38bbc19e3","abstract_canon_sha256":"4e51d7375274de15b919eb443f4654ff33cc47632f6e6fe776144f83e29a5b48"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:36:00.257783Z","signature_b64":"dTz8I+5mhiJ9/OiW3dqWf3a322qgj0w6+VKrW+BAQyyHg3dZCRDf/SKA7EUBkTpgb0IWRgjBbixLM+fMAZAbAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6795b2e7e886f60b8dd365be57741f0625c52f96f5f1d44b06975a6959db6c7","last_reissued_at":"2026-07-05T05:36:00.257294Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:36:00.257294Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian estimation of nonlinear Hawkes process","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Deborah Sulem, Judith Rousseau, Vincent Rivoirard","submitted_at":"2021-03-31T15:28:51Z","abstract_excerpt":"Multivariate point processes are widely applied to model event-type data such as natural disasters, online message exchanges, financial transactions or neuronal spike trains. One very popular point process model in which the probability of occurrences of new events depend on the past of the process is the Hawkes process. In this work we consider the nonlinear Hawkes process, which notably models excitation and inhibition phenomena between dimensions of the process. In a nonparametric Bayesian estimation framework, we obtain concentration rates of the posterior distribution on the parameters, u"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.17164","kind":"arxiv","version":3},"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/2103.17164/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":"2103.17164","created_at":"2026-07-05T05:36:00.257366+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.17164v3","created_at":"2026-07-05T05:36:00.257366+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.17164","created_at":"2026-07-05T05:36:00.257366+00:00"},{"alias_kind":"pith_short_12","alias_value":"YZ4VWLT6RBXW","created_at":"2026-07-05T05:36:00.257366+00:00"},{"alias_kind":"pith_short_16","alias_value":"YZ4VWLT6RBXWBOG5","created_at":"2026-07-05T05:36:00.257366+00:00"},{"alias_kind":"pith_short_8","alias_value":"YZ4VWLT6","created_at":"2026-07-05T05:36:00.257366+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/YZ4VWLT6RBXWBOG5GZN6K52B6B","json":"https://pith.science/pith/YZ4VWLT6RBXWBOG5GZN6K52B6B.json","graph_json":"https://pith.science/api/pith-number/YZ4VWLT6RBXWBOG5GZN6K52B6B/graph.json","events_json":"https://pith.science/api/pith-number/YZ4VWLT6RBXWBOG5GZN6K52B6B/events.json","paper":"https://pith.science/paper/YZ4VWLT6"},"agent_actions":{"view_html":"https://pith.science/pith/YZ4VWLT6RBXWBOG5GZN6K52B6B","download_json":"https://pith.science/pith/YZ4VWLT6RBXWBOG5GZN6K52B6B.json","view_paper":"https://pith.science/paper/YZ4VWLT6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.17164&json=true","fetch_graph":"https://pith.science/api/pith-number/YZ4VWLT6RBXWBOG5GZN6K52B6B/graph.json","fetch_events":"https://pith.science/api/pith-number/YZ4VWLT6RBXWBOG5GZN6K52B6B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YZ4VWLT6RBXWBOG5GZN6K52B6B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YZ4VWLT6RBXWBOG5GZN6K52B6B/action/storage_attestation","attest_author":"https://pith.science/pith/YZ4VWLT6RBXWBOG5GZN6K52B6B/action/author_attestation","sign_citation":"https://pith.science/pith/YZ4VWLT6RBXWBOG5GZN6K52B6B/action/citation_signature","submit_replication":"https://pith.science/pith/YZ4VWLT6RBXWBOG5GZN6K52B6B/action/replication_record"}},"created_at":"2026-07-05T05:36:00.257366+00:00","updated_at":"2026-07-05T05:36:00.257366+00:00"}