{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:OCEJARBVUZ6U2ES3JWT62CYN7J","short_pith_number":"pith:OCEJARBV","schema_version":"1.0","canonical_sha256":"7088904435a67d4d125b4da7ed0b0dfa7e7f7e5c6d3bdd2803b70edcfaf843e7","source":{"kind":"arxiv","id":"1302.3564","version":1},"attestation_state":"computed","paper":{"title":"Tail Sensitivity Analysis in Bayesian Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"cs.AI","authors_text":"Cristina Solares, Enrique F. Castillo, Patricia Gomez","submitted_at":"2013-02-13T14:12:46Z","abstract_excerpt":"The paper presents an efficient method for simulating the tails of a target variable Z=h(X) which depends on a set of basic variables X=(X_1, ..., X_n).  To this aim, variables X_i, i=1, ..., n are sequentially simulated in such a manner that Z=h(x_1, ..., x_i-1, X_i, ..., X_n) is guaranteed to be in the tail of Z.  When this method is difficult to apply, an alternative method is proposed, which leads to a low rejection proportion of sample values, when compared with the Monte Carlo method.  Both methods are shown to be very useful to perform a sensitivity analysis of Bayesian networks, when v"},"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":"1302.3564","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2013-02-13T14:12:46Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"c6fbee27eef3233ff2a3eb54c5a196c28cdf2873cd8dcdd6e370bc62736f2a30","abstract_canon_sha256":"aa155f5e11a69ee065657a471e9cbc5a20ed9f7a304d95d01908c12a230e2a9f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:33:36.824668Z","signature_b64":"58flBeR+vebjXWluZ8GowJrk2zaWMRjdvL1GpQYIS/UJwiyRVVid6KuyBxmD04o4RB/ARfhaM8s0vdhnyV8gDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7088904435a67d4d125b4da7ed0b0dfa7e7f7e5c6d3bdd2803b70edcfaf843e7","last_reissued_at":"2026-05-18T03:33:36.823929Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:33:36.823929Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tail Sensitivity Analysis in Bayesian Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"cs.AI","authors_text":"Cristina Solares, Enrique F. Castillo, Patricia Gomez","submitted_at":"2013-02-13T14:12:46Z","abstract_excerpt":"The paper presents an efficient method for simulating the tails of a target variable Z=h(X) which depends on a set of basic variables X=(X_1, ..., X_n).  To this aim, variables X_i, i=1, ..., n are sequentially simulated in such a manner that Z=h(x_1, ..., x_i-1, X_i, ..., X_n) is guaranteed to be in the tail of Z.  When this method is difficult to apply, an alternative method is proposed, which leads to a low rejection proportion of sample values, when compared with the Monte Carlo method.  Both methods are shown to be very useful to perform a sensitivity analysis of Bayesian networks, when v"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1302.3564","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":"1302.3564","created_at":"2026-05-18T03:33:36.824022+00:00"},{"alias_kind":"arxiv_version","alias_value":"1302.3564v1","created_at":"2026-05-18T03:33:36.824022+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1302.3564","created_at":"2026-05-18T03:33:36.824022+00:00"},{"alias_kind":"pith_short_12","alias_value":"OCEJARBVUZ6U","created_at":"2026-05-18T12:27:54.935989+00:00"},{"alias_kind":"pith_short_16","alias_value":"OCEJARBVUZ6U2ES3","created_at":"2026-05-18T12:27:54.935989+00:00"},{"alias_kind":"pith_short_8","alias_value":"OCEJARBV","created_at":"2026-05-18T12:27:54.935989+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/OCEJARBVUZ6U2ES3JWT62CYN7J","json":"https://pith.science/pith/OCEJARBVUZ6U2ES3JWT62CYN7J.json","graph_json":"https://pith.science/api/pith-number/OCEJARBVUZ6U2ES3JWT62CYN7J/graph.json","events_json":"https://pith.science/api/pith-number/OCEJARBVUZ6U2ES3JWT62CYN7J/events.json","paper":"https://pith.science/paper/OCEJARBV"},"agent_actions":{"view_html":"https://pith.science/pith/OCEJARBVUZ6U2ES3JWT62CYN7J","download_json":"https://pith.science/pith/OCEJARBVUZ6U2ES3JWT62CYN7J.json","view_paper":"https://pith.science/paper/OCEJARBV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1302.3564&json=true","fetch_graph":"https://pith.science/api/pith-number/OCEJARBVUZ6U2ES3JWT62CYN7J/graph.json","fetch_events":"https://pith.science/api/pith-number/OCEJARBVUZ6U2ES3JWT62CYN7J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OCEJARBVUZ6U2ES3JWT62CYN7J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OCEJARBVUZ6U2ES3JWT62CYN7J/action/storage_attestation","attest_author":"https://pith.science/pith/OCEJARBVUZ6U2ES3JWT62CYN7J/action/author_attestation","sign_citation":"https://pith.science/pith/OCEJARBVUZ6U2ES3JWT62CYN7J/action/citation_signature","submit_replication":"https://pith.science/pith/OCEJARBVUZ6U2ES3JWT62CYN7J/action/replication_record"}},"created_at":"2026-05-18T03:33:36.824022+00:00","updated_at":"2026-05-18T03:33:36.824022+00:00"}