{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:3WBU5YVNUYF5BSSKYI7E7XQWPI","short_pith_number":"pith:3WBU5YVN","schema_version":"1.0","canonical_sha256":"dd834ee2ada60bd0ca4ac23e4fde167a19d121e6a9e34c5c206bad77fed66673","source":{"kind":"arxiv","id":"2109.06567","version":1},"attestation_state":"computed","paper":{"title":"Gibbs posterior inference on a Levy density under discrete sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR","q-fin.MF","stat.TH"],"primary_cat":"math.ST","authors_text":"Ryan Martin, Zhe Wang","submitted_at":"2021-09-14T10:26:55Z","abstract_excerpt":"In mathematical finance, Levy processes are widely used for their ability to model both continuous variation and abrupt, discontinuous jumps. These jumps are practically relevant, so reliable inference on the feature that controls jump frequencies and magnitudes, namely, the Levy density, is of critical importance. A specific obstacle to carrying out model-based (e.g., Bayesian) inference in such problems is that, for general Levy processes, the likelihood is intractable. To overcome this obstacle, here we adopt a Gibbs posterior framework that updates a prior distribution using a suitable los"},"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":"2109.06567","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2021-09-14T10:26:55Z","cross_cats_sorted":["math.PR","q-fin.MF","stat.TH"],"title_canon_sha256":"e60335f1983850d19a4d08bf6fca4f399c590b697a1f51ee0c1b2b952e9f1f30","abstract_canon_sha256":"34e541748edf51ca03505e17543748be26e35b1792c8907957a0fba12e42ec62"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:15:37.329847Z","signature_b64":"48N+0pyxtfGS4R9wPTjpi/TRRbJyBkTP/EFl2mOGy8rG5RyP97zDldThxorzKrHMiQk0T78PoEKCot3q4vgZBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dd834ee2ada60bd0ca4ac23e4fde167a19d121e6a9e34c5c206bad77fed66673","last_reissued_at":"2026-07-05T03:15:37.329429Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:15:37.329429Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gibbs posterior inference on a Levy density under discrete sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR","q-fin.MF","stat.TH"],"primary_cat":"math.ST","authors_text":"Ryan Martin, Zhe Wang","submitted_at":"2021-09-14T10:26:55Z","abstract_excerpt":"In mathematical finance, Levy processes are widely used for their ability to model both continuous variation and abrupt, discontinuous jumps. These jumps are practically relevant, so reliable inference on the feature that controls jump frequencies and magnitudes, namely, the Levy density, is of critical importance. A specific obstacle to carrying out model-based (e.g., Bayesian) inference in such problems is that, for general Levy processes, the likelihood is intractable. To overcome this obstacle, here we adopt a Gibbs posterior framework that updates a prior distribution using a suitable los"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.06567","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2109.06567/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":"2109.06567","created_at":"2026-07-05T03:15:37.329485+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.06567v1","created_at":"2026-07-05T03:15:37.329485+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.06567","created_at":"2026-07-05T03:15:37.329485+00:00"},{"alias_kind":"pith_short_12","alias_value":"3WBU5YVNUYF5","created_at":"2026-07-05T03:15:37.329485+00:00"},{"alias_kind":"pith_short_16","alias_value":"3WBU5YVNUYF5BSSK","created_at":"2026-07-05T03:15:37.329485+00:00"},{"alias_kind":"pith_short_8","alias_value":"3WBU5YVN","created_at":"2026-07-05T03:15:37.329485+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.22587","citing_title":"Bayesian Non-Parametric Inference for L\\'evy Measures in State-Space Models","ref_index":59,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3WBU5YVNUYF5BSSKYI7E7XQWPI","json":"https://pith.science/pith/3WBU5YVNUYF5BSSKYI7E7XQWPI.json","graph_json":"https://pith.science/api/pith-number/3WBU5YVNUYF5BSSKYI7E7XQWPI/graph.json","events_json":"https://pith.science/api/pith-number/3WBU5YVNUYF5BSSKYI7E7XQWPI/events.json","paper":"https://pith.science/paper/3WBU5YVN"},"agent_actions":{"view_html":"https://pith.science/pith/3WBU5YVNUYF5BSSKYI7E7XQWPI","download_json":"https://pith.science/pith/3WBU5YVNUYF5BSSKYI7E7XQWPI.json","view_paper":"https://pith.science/paper/3WBU5YVN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.06567&json=true","fetch_graph":"https://pith.science/api/pith-number/3WBU5YVNUYF5BSSKYI7E7XQWPI/graph.json","fetch_events":"https://pith.science/api/pith-number/3WBU5YVNUYF5BSSKYI7E7XQWPI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3WBU5YVNUYF5BSSKYI7E7XQWPI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3WBU5YVNUYF5BSSKYI7E7XQWPI/action/storage_attestation","attest_author":"https://pith.science/pith/3WBU5YVNUYF5BSSKYI7E7XQWPI/action/author_attestation","sign_citation":"https://pith.science/pith/3WBU5YVNUYF5BSSKYI7E7XQWPI/action/citation_signature","submit_replication":"https://pith.science/pith/3WBU5YVNUYF5BSSKYI7E7XQWPI/action/replication_record"}},"created_at":"2026-07-05T03:15:37.329485+00:00","updated_at":"2026-07-05T03:15:37.329485+00:00"}