{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:D4EZD6RC7APAXXGOEJ2DRN5DAG","short_pith_number":"pith:D4EZD6RC","schema_version":"1.0","canonical_sha256":"1f0991fa22f81e0bdcce227438b7a30191889f0e313f6f5b1657d1eb3baedd96","source":{"kind":"arxiv","id":"1807.01152","version":1},"attestation_state":"computed","paper":{"title":"Probability Based Independence Sampler for Bayesian Quantitative Learning in Graphical Log-Linear Marginal Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Claudia Tarantola, Ioannis Ntzoufras, Monia Lupparelli","submitted_at":"2018-07-03T13:21:05Z","abstract_excerpt":"Bayesian methods for graphical log-linear marginal models have not been developed in the same extent as traditional frequentist approaches. In this work, we introduce a novel Bayesian approach for quantitative learning for such models. These models belong to curved exponential families that are difficult to handle from a Bayesian perspective. Furthermore, the likelihood cannot be analytically expressed as a function of the marginal log-linear interactions, but only in terms of cell counts or probabilities.\n  Posterior distributions cannot be directly obtained, and MCMC methods are needed. Fina"},"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":"1807.01152","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-07-03T13:21:05Z","cross_cats_sorted":[],"title_canon_sha256":"6f726088b7def9f4d6cb1b2e038bfdf18d89de14d63a35396bca5910aab71194","abstract_canon_sha256":"f84737c14b613a857946738fabe39ef784d1a11bd32d3ab82f5da281c50dcb9b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:45.127367Z","signature_b64":"1YqNJdN599kV5R2P7qseGsZiudXYBrhB9LHvitLdlKQa7u6SywneAgEPmWNcCKViePnuvgzGTQ/2JpmbsQZuAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f0991fa22f81e0bdcce227438b7a30191889f0e313f6f5b1657d1eb3baedd96","last_reissued_at":"2026-05-18T00:11:45.126613Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:45.126613Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Probability Based Independence Sampler for Bayesian Quantitative Learning in Graphical Log-Linear Marginal Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Claudia Tarantola, Ioannis Ntzoufras, Monia Lupparelli","submitted_at":"2018-07-03T13:21:05Z","abstract_excerpt":"Bayesian methods for graphical log-linear marginal models have not been developed in the same extent as traditional frequentist approaches. In this work, we introduce a novel Bayesian approach for quantitative learning for such models. These models belong to curved exponential families that are difficult to handle from a Bayesian perspective. Furthermore, the likelihood cannot be analytically expressed as a function of the marginal log-linear interactions, but only in terms of cell counts or probabilities.\n  Posterior distributions cannot be directly obtained, and MCMC methods are needed. Fina"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.01152","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":"1807.01152","created_at":"2026-05-18T00:11:45.126742+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.01152v1","created_at":"2026-05-18T00:11:45.126742+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.01152","created_at":"2026-05-18T00:11:45.126742+00:00"},{"alias_kind":"pith_short_12","alias_value":"D4EZD6RC7APA","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"D4EZD6RC7APAXXGO","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"D4EZD6RC","created_at":"2026-05-18T12:32:19.392346+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/D4EZD6RC7APAXXGOEJ2DRN5DAG","json":"https://pith.science/pith/D4EZD6RC7APAXXGOEJ2DRN5DAG.json","graph_json":"https://pith.science/api/pith-number/D4EZD6RC7APAXXGOEJ2DRN5DAG/graph.json","events_json":"https://pith.science/api/pith-number/D4EZD6RC7APAXXGOEJ2DRN5DAG/events.json","paper":"https://pith.science/paper/D4EZD6RC"},"agent_actions":{"view_html":"https://pith.science/pith/D4EZD6RC7APAXXGOEJ2DRN5DAG","download_json":"https://pith.science/pith/D4EZD6RC7APAXXGOEJ2DRN5DAG.json","view_paper":"https://pith.science/paper/D4EZD6RC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.01152&json=true","fetch_graph":"https://pith.science/api/pith-number/D4EZD6RC7APAXXGOEJ2DRN5DAG/graph.json","fetch_events":"https://pith.science/api/pith-number/D4EZD6RC7APAXXGOEJ2DRN5DAG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D4EZD6RC7APAXXGOEJ2DRN5DAG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D4EZD6RC7APAXXGOEJ2DRN5DAG/action/storage_attestation","attest_author":"https://pith.science/pith/D4EZD6RC7APAXXGOEJ2DRN5DAG/action/author_attestation","sign_citation":"https://pith.science/pith/D4EZD6RC7APAXXGOEJ2DRN5DAG/action/citation_signature","submit_replication":"https://pith.science/pith/D4EZD6RC7APAXXGOEJ2DRN5DAG/action/replication_record"}},"created_at":"2026-05-18T00:11:45.126742+00:00","updated_at":"2026-05-18T00:11:45.126742+00:00"}