{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:IGS55GSWW5JGHN7FDMFVT57QCV","short_pith_number":"pith:IGS55GSW","schema_version":"1.0","canonical_sha256":"41a5de9a56b75263b7e51b0b59f7f01577b5fe9c8c596b60b15c4e77a10d1a4b","source":{"kind":"arxiv","id":"1205.2746","version":1},"attestation_state":"computed","paper":{"title":"A Multivariate Graphical Stochastic Volatility Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Alex Lenkoski, Yuan Cheng","submitted_at":"2012-05-12T07:50:17Z","abstract_excerpt":"The Gaussian Graphical Model (GGM) is a popular tool for incorporating sparsity into joint multivariate distributions. The G-Wishart distribution, a conjugate prior for precision matrices satisfying general GGM constraints, has now been in existence for over a decade. However, due to the lack of a direct sampler, its use has been limited in hierarchical Bayesian contexts, relegating mixing over the class of GGMs mostly to situations involving standard Gaussian likelihoods. Recent work, however, has developed methods that couple model and parameter moves, first through reversible jump methods a"},"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":"1205.2746","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2012-05-12T07:50:17Z","cross_cats_sorted":[],"title_canon_sha256":"f09da25dd84168b2058ea5a165b9d827048a66d71abf1fc057b2030659adaf95","abstract_canon_sha256":"c215668ebaacb2fa249455223063160b8592535d53fe84c28feae651bad6c88c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:55:46.104715Z","signature_b64":"he5GMtPjYaQnc8anyfXDfiiRlO2/sqYQ0eo83UZh+hLucueScFx5irMoL62FFAOSDZuIiz8n4yVvff0nTZuFBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"41a5de9a56b75263b7e51b0b59f7f01577b5fe9c8c596b60b15c4e77a10d1a4b","last_reissued_at":"2026-05-18T03:55:46.104245Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:55:46.104245Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Multivariate Graphical Stochastic Volatility Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Alex Lenkoski, Yuan Cheng","submitted_at":"2012-05-12T07:50:17Z","abstract_excerpt":"The Gaussian Graphical Model (GGM) is a popular tool for incorporating sparsity into joint multivariate distributions. The G-Wishart distribution, a conjugate prior for precision matrices satisfying general GGM constraints, has now been in existence for over a decade. However, due to the lack of a direct sampler, its use has been limited in hierarchical Bayesian contexts, relegating mixing over the class of GGMs mostly to situations involving standard Gaussian likelihoods. Recent work, however, has developed methods that couple model and parameter moves, first through reversible jump methods a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1205.2746","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":"1205.2746","created_at":"2026-05-18T03:55:46.104309+00:00"},{"alias_kind":"arxiv_version","alias_value":"1205.2746v1","created_at":"2026-05-18T03:55:46.104309+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1205.2746","created_at":"2026-05-18T03:55:46.104309+00:00"},{"alias_kind":"pith_short_12","alias_value":"IGS55GSWW5JG","created_at":"2026-05-18T12:27:09.501522+00:00"},{"alias_kind":"pith_short_16","alias_value":"IGS55GSWW5JGHN7F","created_at":"2026-05-18T12:27:09.501522+00:00"},{"alias_kind":"pith_short_8","alias_value":"IGS55GSW","created_at":"2026-05-18T12:27:09.501522+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/IGS55GSWW5JGHN7FDMFVT57QCV","json":"https://pith.science/pith/IGS55GSWW5JGHN7FDMFVT57QCV.json","graph_json":"https://pith.science/api/pith-number/IGS55GSWW5JGHN7FDMFVT57QCV/graph.json","events_json":"https://pith.science/api/pith-number/IGS55GSWW5JGHN7FDMFVT57QCV/events.json","paper":"https://pith.science/paper/IGS55GSW"},"agent_actions":{"view_html":"https://pith.science/pith/IGS55GSWW5JGHN7FDMFVT57QCV","download_json":"https://pith.science/pith/IGS55GSWW5JGHN7FDMFVT57QCV.json","view_paper":"https://pith.science/paper/IGS55GSW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1205.2746&json=true","fetch_graph":"https://pith.science/api/pith-number/IGS55GSWW5JGHN7FDMFVT57QCV/graph.json","fetch_events":"https://pith.science/api/pith-number/IGS55GSWW5JGHN7FDMFVT57QCV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IGS55GSWW5JGHN7FDMFVT57QCV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IGS55GSWW5JGHN7FDMFVT57QCV/action/storage_attestation","attest_author":"https://pith.science/pith/IGS55GSWW5JGHN7FDMFVT57QCV/action/author_attestation","sign_citation":"https://pith.science/pith/IGS55GSWW5JGHN7FDMFVT57QCV/action/citation_signature","submit_replication":"https://pith.science/pith/IGS55GSWW5JGHN7FDMFVT57QCV/action/replication_record"}},"created_at":"2026-05-18T03:55:46.104309+00:00","updated_at":"2026-05-18T03:55:46.104309+00:00"}