{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:YZSJSVNQMZSNBJWMW4V3P6EFJZ","short_pith_number":"pith:YZSJSVNQ","schema_version":"1.0","canonical_sha256":"c6649955b06664d0a6ccb72bb7f8854e62c9569c2106c79ab1d753c3e9ccbd4b","source":{"kind":"arxiv","id":"1209.6283","version":2},"attestation_state":"computed","paper":{"title":"Geometric Ergodicity & Scanning Strategies For Two-Component Gibbs Samplers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Alicia A. Johnson, Owen Burbank","submitted_at":"2012-09-27T16:52:03Z","abstract_excerpt":"In any Markov chain Monte Carlo analysis, rapid convergence of the chain to its target probability distribution is of practical and theoretical importance. A chain that converges at a geometric rate is geometrically ergodic. In this paper, we explore geometric ergodicity for two-component Gibbs samplers which, under a chosen scanning strategy, evolve by combining one-at-a-time updates of the two components. We compare convergence behaviors between and within three such strategies: composition, random sequence scan, and random scan. Our main results are twofold. First, we establish that if the "},"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":"1209.6283","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2012-09-27T16:52:03Z","cross_cats_sorted":[],"title_canon_sha256":"128f0c0ca5da991a820c615ab023f0cdbb42b6881a337530a9ae9b04a13fea6b","abstract_canon_sha256":"8989a663240ed5b3fc9333cf4eac959de99d0b48c4dca514adb1db5214f36be8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:44:05.093243Z","signature_b64":"Nq5Z6f3ZgG5dDDQSdxXDAYvTFKtKzlJTeHkEG9/J8P5/F+vxli+Noda33GNhqQZ+MVlYZSg02yaKSxfmn/3WAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6649955b06664d0a6ccb72bb7f8854e62c9569c2106c79ab1d753c3e9ccbd4b","last_reissued_at":"2026-05-18T03:44:05.092576Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:44:05.092576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Geometric Ergodicity & Scanning Strategies For Two-Component Gibbs Samplers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Alicia A. Johnson, Owen Burbank","submitted_at":"2012-09-27T16:52:03Z","abstract_excerpt":"In any Markov chain Monte Carlo analysis, rapid convergence of the chain to its target probability distribution is of practical and theoretical importance. A chain that converges at a geometric rate is geometrically ergodic. In this paper, we explore geometric ergodicity for two-component Gibbs samplers which, under a chosen scanning strategy, evolve by combining one-at-a-time updates of the two components. We compare convergence behaviors between and within three such strategies: composition, random sequence scan, and random scan. Our main results are twofold. First, we establish that if the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1209.6283","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":"1209.6283","created_at":"2026-05-18T03:44:05.092669+00:00"},{"alias_kind":"arxiv_version","alias_value":"1209.6283v2","created_at":"2026-05-18T03:44:05.092669+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1209.6283","created_at":"2026-05-18T03:44:05.092669+00:00"},{"alias_kind":"pith_short_12","alias_value":"YZSJSVNQMZSN","created_at":"2026-05-18T12:27:30.460161+00:00"},{"alias_kind":"pith_short_16","alias_value":"YZSJSVNQMZSNBJWM","created_at":"2026-05-18T12:27:30.460161+00:00"},{"alias_kind":"pith_short_8","alias_value":"YZSJSVNQ","created_at":"2026-05-18T12:27:30.460161+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/YZSJSVNQMZSNBJWMW4V3P6EFJZ","json":"https://pith.science/pith/YZSJSVNQMZSNBJWMW4V3P6EFJZ.json","graph_json":"https://pith.science/api/pith-number/YZSJSVNQMZSNBJWMW4V3P6EFJZ/graph.json","events_json":"https://pith.science/api/pith-number/YZSJSVNQMZSNBJWMW4V3P6EFJZ/events.json","paper":"https://pith.science/paper/YZSJSVNQ"},"agent_actions":{"view_html":"https://pith.science/pith/YZSJSVNQMZSNBJWMW4V3P6EFJZ","download_json":"https://pith.science/pith/YZSJSVNQMZSNBJWMW4V3P6EFJZ.json","view_paper":"https://pith.science/paper/YZSJSVNQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1209.6283&json=true","fetch_graph":"https://pith.science/api/pith-number/YZSJSVNQMZSNBJWMW4V3P6EFJZ/graph.json","fetch_events":"https://pith.science/api/pith-number/YZSJSVNQMZSNBJWMW4V3P6EFJZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YZSJSVNQMZSNBJWMW4V3P6EFJZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YZSJSVNQMZSNBJWMW4V3P6EFJZ/action/storage_attestation","attest_author":"https://pith.science/pith/YZSJSVNQMZSNBJWMW4V3P6EFJZ/action/author_attestation","sign_citation":"https://pith.science/pith/YZSJSVNQMZSNBJWMW4V3P6EFJZ/action/citation_signature","submit_replication":"https://pith.science/pith/YZSJSVNQMZSNBJWMW4V3P6EFJZ/action/replication_record"}},"created_at":"2026-05-18T03:44:05.092669+00:00","updated_at":"2026-05-18T03:44:05.092669+00:00"}