{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:R2MOPTWN6RE3VQX2DS4AR6VANK","short_pith_number":"pith:R2MOPTWN","schema_version":"1.0","canonical_sha256":"8e98e7cecdf449bac2fa1cb808faa06a86cc14aecce17cdabb404f62ac4a88f6","source":{"kind":"arxiv","id":"1811.09436","version":2},"attestation_state":"computed","paper":{"title":"A weight-bounded importance sampling method for variance reduction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Jinglai Li, Linjun Lu, Tengchao Yu","submitted_at":"2018-11-23T11:36:57Z","abstract_excerpt":"Importance sampling (IS) is an important technique to reduce the estimation variance in Monte Carlo simulations. In many practical problems, however, the use of IS method may result in unbounded variance, and thus fail to provide reliable estimates. To address the issue, we propose a method which can prevent the risk of unbounded variance; the proposed method performs the standard IS for the integral of interest in a region only in which the IS weight is bounded and use the result as an approximation to the original integral. It can be verified that the resulting estimator has a finite varianc"},"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":"1811.09436","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2018-11-23T11:36:57Z","cross_cats_sorted":[],"title_canon_sha256":"3c0f0c639ee639f84521a971cb550db013c7697a8b2fda0ff0d4f20e9b0b6121","abstract_canon_sha256":"d699b0421dca7256e4a5b4d784c5a8fcf3d96117bb61d8b69b38fcceb9775861"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:51.509542Z","signature_b64":"ml/VdScuIEc14bg6sBIz4peJiubQl55dLqto63Gg8nGRnyD/9lTOFE5oI1HWchjh4lTaE1fnwjQN5DpBY9IlDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e98e7cecdf449bac2fa1cb808faa06a86cc14aecce17cdabb404f62ac4a88f6","last_reissued_at":"2026-05-17T23:52:51.508742Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:51.508742Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A weight-bounded importance sampling method for variance reduction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Jinglai Li, Linjun Lu, Tengchao Yu","submitted_at":"2018-11-23T11:36:57Z","abstract_excerpt":"Importance sampling (IS) is an important technique to reduce the estimation variance in Monte Carlo simulations. In many practical problems, however, the use of IS method may result in unbounded variance, and thus fail to provide reliable estimates. To address the issue, we propose a method which can prevent the risk of unbounded variance; the proposed method performs the standard IS for the integral of interest in a region only in which the IS weight is bounded and use the result as an approximation to the original integral. It can be verified that the resulting estimator has a finite varianc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.09436","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":"1811.09436","created_at":"2026-05-17T23:52:51.508885+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.09436v2","created_at":"2026-05-17T23:52:51.508885+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.09436","created_at":"2026-05-17T23:52:51.508885+00:00"},{"alias_kind":"pith_short_12","alias_value":"R2MOPTWN6RE3","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_16","alias_value":"R2MOPTWN6RE3VQX2","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_8","alias_value":"R2MOPTWN","created_at":"2026-05-18T12:32:50.500415+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/R2MOPTWN6RE3VQX2DS4AR6VANK","json":"https://pith.science/pith/R2MOPTWN6RE3VQX2DS4AR6VANK.json","graph_json":"https://pith.science/api/pith-number/R2MOPTWN6RE3VQX2DS4AR6VANK/graph.json","events_json":"https://pith.science/api/pith-number/R2MOPTWN6RE3VQX2DS4AR6VANK/events.json","paper":"https://pith.science/paper/R2MOPTWN"},"agent_actions":{"view_html":"https://pith.science/pith/R2MOPTWN6RE3VQX2DS4AR6VANK","download_json":"https://pith.science/pith/R2MOPTWN6RE3VQX2DS4AR6VANK.json","view_paper":"https://pith.science/paper/R2MOPTWN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.09436&json=true","fetch_graph":"https://pith.science/api/pith-number/R2MOPTWN6RE3VQX2DS4AR6VANK/graph.json","fetch_events":"https://pith.science/api/pith-number/R2MOPTWN6RE3VQX2DS4AR6VANK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R2MOPTWN6RE3VQX2DS4AR6VANK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R2MOPTWN6RE3VQX2DS4AR6VANK/action/storage_attestation","attest_author":"https://pith.science/pith/R2MOPTWN6RE3VQX2DS4AR6VANK/action/author_attestation","sign_citation":"https://pith.science/pith/R2MOPTWN6RE3VQX2DS4AR6VANK/action/citation_signature","submit_replication":"https://pith.science/pith/R2MOPTWN6RE3VQX2DS4AR6VANK/action/replication_record"}},"created_at":"2026-05-17T23:52:51.508885+00:00","updated_at":"2026-05-17T23:52:51.508885+00:00"}