{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:HFPMB2EF7C5HU4DX5BB5SJLD3D","short_pith_number":"pith:HFPMB2EF","schema_version":"1.0","canonical_sha256":"395ec0e885f8ba7a7077e843d92563d8f19d4a76039c03d219f1e9958e35366c","source":{"kind":"arxiv","id":"1508.06700","version":2},"attestation_state":"computed","paper":{"title":"A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"math.NA","authors_text":"Jinglai Li, Keyi Wu","submitted_at":"2015-08-27T02:11:37Z","abstract_excerpt":"In this work we consider a class of uncertainty quantification problems where the system performance or reliability is characterized by a scalar parameter $y$. The performance parameter $y$ is random due to the presence of various sources of uncertainty in the system, and our goal is to estimate the probability density function (PDF) of $y$. We propose to use the multicanonical Monte Carlo (MMC) method, a special type of adaptive importance sampling algorithm, to compute the PDF of interest. Moreover, we develop an adaptive algorithm to construct local Gaussian process surrogates to further ac"},"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":"1508.06700","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2015-08-27T02:11:37Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"1e75b7de680da7317909b7f7fa0a03685d7ffa2f891ba2311f1d006df73a9b9c","abstract_canon_sha256":"20a64866895615b5bbf3ad17beec494753ef2ec84b534e903bba26abc4d8e2ff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:10:52.536589Z","signature_b64":"OByKF8RQsrxywT2Ax+CWKhT/EnmaA3hQI0ZNHAkRDN8WJSOrLSwFtVVufJaCgMJFJMEmdyzwUrexXs2w3IEIBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"395ec0e885f8ba7a7077e843d92563d8f19d4a76039c03d219f1e9958e35366c","last_reissued_at":"2026-05-18T01:10:52.536110Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:10:52.536110Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"math.NA","authors_text":"Jinglai Li, Keyi Wu","submitted_at":"2015-08-27T02:11:37Z","abstract_excerpt":"In this work we consider a class of uncertainty quantification problems where the system performance or reliability is characterized by a scalar parameter $y$. The performance parameter $y$ is random due to the presence of various sources of uncertainty in the system, and our goal is to estimate the probability density function (PDF) of $y$. We propose to use the multicanonical Monte Carlo (MMC) method, a special type of adaptive importance sampling algorithm, to compute the PDF of interest. Moreover, we develop an adaptive algorithm to construct local Gaussian process surrogates to further ac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1508.06700","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":"1508.06700","created_at":"2026-05-18T01:10:52.536176+00:00"},{"alias_kind":"arxiv_version","alias_value":"1508.06700v2","created_at":"2026-05-18T01:10:52.536176+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1508.06700","created_at":"2026-05-18T01:10:52.536176+00:00"},{"alias_kind":"pith_short_12","alias_value":"HFPMB2EF7C5H","created_at":"2026-05-18T12:29:25.134429+00:00"},{"alias_kind":"pith_short_16","alias_value":"HFPMB2EF7C5HU4DX","created_at":"2026-05-18T12:29:25.134429+00:00"},{"alias_kind":"pith_short_8","alias_value":"HFPMB2EF","created_at":"2026-05-18T12:29:25.134429+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/HFPMB2EF7C5HU4DX5BB5SJLD3D","json":"https://pith.science/pith/HFPMB2EF7C5HU4DX5BB5SJLD3D.json","graph_json":"https://pith.science/api/pith-number/HFPMB2EF7C5HU4DX5BB5SJLD3D/graph.json","events_json":"https://pith.science/api/pith-number/HFPMB2EF7C5HU4DX5BB5SJLD3D/events.json","paper":"https://pith.science/paper/HFPMB2EF"},"agent_actions":{"view_html":"https://pith.science/pith/HFPMB2EF7C5HU4DX5BB5SJLD3D","download_json":"https://pith.science/pith/HFPMB2EF7C5HU4DX5BB5SJLD3D.json","view_paper":"https://pith.science/paper/HFPMB2EF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1508.06700&json=true","fetch_graph":"https://pith.science/api/pith-number/HFPMB2EF7C5HU4DX5BB5SJLD3D/graph.json","fetch_events":"https://pith.science/api/pith-number/HFPMB2EF7C5HU4DX5BB5SJLD3D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HFPMB2EF7C5HU4DX5BB5SJLD3D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HFPMB2EF7C5HU4DX5BB5SJLD3D/action/storage_attestation","attest_author":"https://pith.science/pith/HFPMB2EF7C5HU4DX5BB5SJLD3D/action/author_attestation","sign_citation":"https://pith.science/pith/HFPMB2EF7C5HU4DX5BB5SJLD3D/action/citation_signature","submit_replication":"https://pith.science/pith/HFPMB2EF7C5HU4DX5BB5SJLD3D/action/replication_record"}},"created_at":"2026-05-18T01:10:52.536176+00:00","updated_at":"2026-05-18T01:10:52.536176+00:00"}