{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2011:RT26H6WVMDIENPQ35VVFXRTE4G","short_pith_number":"pith:RT26H6WV","canonical_record":{"source":{"id":"1111.0317","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2011-11-01T21:06:48Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"73317f03093fbdfbaa5cef1906a757ffd00983792d2302b9fc2ed975c11b26ba","abstract_canon_sha256":"9ad8f51b0851b5b3fbcfd0c2b51cd0d8b7842857d2688347f4e308cbdd16cc1e"},"schema_version":"1.0"},"canonical_sha256":"8cf5e3fad560d046be1bed6a5bc664e18d7ea408603ab54eac78e46ec7210fbb","source":{"kind":"arxiv","id":"1111.0317","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1111.0317","created_at":"2026-05-18T03:36:46Z"},{"alias_kind":"arxiv_version","alias_value":"1111.0317v2","created_at":"2026-05-18T03:36:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1111.0317","created_at":"2026-05-18T03:36:46Z"},{"alias_kind":"pith_short_12","alias_value":"RT26H6WVMDIE","created_at":"2026-05-18T12:26:41Z"},{"alias_kind":"pith_short_16","alias_value":"RT26H6WVMDIENPQ3","created_at":"2026-05-18T12:26:41Z"},{"alias_kind":"pith_short_8","alias_value":"RT26H6WV","created_at":"2026-05-18T12:26:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2011:RT26H6WVMDIENPQ35VVFXRTE4G","target":"record","payload":{"canonical_record":{"source":{"id":"1111.0317","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2011-11-01T21:06:48Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"73317f03093fbdfbaa5cef1906a757ffd00983792d2302b9fc2ed975c11b26ba","abstract_canon_sha256":"9ad8f51b0851b5b3fbcfd0c2b51cd0d8b7842857d2688347f4e308cbdd16cc1e"},"schema_version":"1.0"},"canonical_sha256":"8cf5e3fad560d046be1bed6a5bc664e18d7ea408603ab54eac78e46ec7210fbb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:36:46.636247Z","signature_b64":"yL8USqLl3+QJKqu9pZErH/eliw7vtMcwuXdSzrWzIeKdlffAfz8BRVINhP01DTJAbkezswlfcFhYImPDx0RpAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8cf5e3fad560d046be1bed6a5bc664e18d7ea408603ab54eac78e46ec7210fbb","last_reissued_at":"2026-05-18T03:36:46.635531Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:36:46.635531Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1111.0317","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:36:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"za50AVB5QACZzH+KVm3E3HqQKWJorwDTalAmJqpMHL54/F2Oz8zfg+nq+KpvZ7bI373X5ezKUvavBUU8atw2Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T03:44:14.565461Z"},"content_sha256":"5e842f02df6f72c358f6c589505a056f1e982ccd52a5cc57518a25a9c881bf13","schema_version":"1.0","event_id":"sha256:5e842f02df6f72c358f6c589505a056f1e982ccd52a5cc57518a25a9c881bf13"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2011:RT26H6WVMDIENPQ35VVFXRTE4G","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian Gaussian Copula Factor Models for Mixed Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"David B. Dunson, Jared S. Murray, Joseph E. Lucas, Lawrence Carin","submitted_at":"2011-11-01T21:06:48Z","abstract_excerpt":"Gaussian factor models have proven widely useful for parsimoniously characterizing dependence in multivariate data. There is a rich literature on their extension to mixed categorical and continuous variables, using latent Gaussian variables or through generalized latent trait models acommodating measurements in the exponential family. However, when generalizing to non-Gaussian measured variables the latent variables typically influence both the dependence structure and the form of the marginal distributions, complicating interpretation and introducing artifacts. To address this problem we prop"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1111.0317","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:36:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iiXibIYmiw1wSyTYg2sxaWWNahOFjMo5W9MMaV9HpXYiP/qoQYsjCj0id6VWldRFVFvI1AXF07bsMgt5TD2qBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T03:44:14.566139Z"},"content_sha256":"3b0414d8d816d339869ce61ffd4fa664376cadb2c741ebd0a6c38da1cd895145","schema_version":"1.0","event_id":"sha256:3b0414d8d816d339869ce61ffd4fa664376cadb2c741ebd0a6c38da1cd895145"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RT26H6WVMDIENPQ35VVFXRTE4G/bundle.json","state_url":"https://pith.science/pith/RT26H6WVMDIENPQ35VVFXRTE4G/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RT26H6WVMDIENPQ35VVFXRTE4G/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-26T03:44:14Z","links":{"resolver":"https://pith.science/pith/RT26H6WVMDIENPQ35VVFXRTE4G","bundle":"https://pith.science/pith/RT26H6WVMDIENPQ35VVFXRTE4G/bundle.json","state":"https://pith.science/pith/RT26H6WVMDIENPQ35VVFXRTE4G/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RT26H6WVMDIENPQ35VVFXRTE4G/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2011:RT26H6WVMDIENPQ35VVFXRTE4G","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9ad8f51b0851b5b3fbcfd0c2b51cd0d8b7842857d2688347f4e308cbdd16cc1e","cross_cats_sorted":["stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2011-11-01T21:06:48Z","title_canon_sha256":"73317f03093fbdfbaa5cef1906a757ffd00983792d2302b9fc2ed975c11b26ba"},"schema_version":"1.0","source":{"id":"1111.0317","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1111.0317","created_at":"2026-05-18T03:36:46Z"},{"alias_kind":"arxiv_version","alias_value":"1111.0317v2","created_at":"2026-05-18T03:36:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1111.0317","created_at":"2026-05-18T03:36:46Z"},{"alias_kind":"pith_short_12","alias_value":"RT26H6WVMDIE","created_at":"2026-05-18T12:26:41Z"},{"alias_kind":"pith_short_16","alias_value":"RT26H6WVMDIENPQ3","created_at":"2026-05-18T12:26:41Z"},{"alias_kind":"pith_short_8","alias_value":"RT26H6WV","created_at":"2026-05-18T12:26:41Z"}],"graph_snapshots":[{"event_id":"sha256:3b0414d8d816d339869ce61ffd4fa664376cadb2c741ebd0a6c38da1cd895145","target":"graph","created_at":"2026-05-18T03:36:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Gaussian factor models have proven widely useful for parsimoniously characterizing dependence in multivariate data. There is a rich literature on their extension to mixed categorical and continuous variables, using latent Gaussian variables or through generalized latent trait models acommodating measurements in the exponential family. However, when generalizing to non-Gaussian measured variables the latent variables typically influence both the dependence structure and the form of the marginal distributions, complicating interpretation and introducing artifacts. To address this problem we prop","authors_text":"David B. Dunson, Jared S. Murray, Joseph E. Lucas, Lawrence Carin","cross_cats":["stat.AP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2011-11-01T21:06:48Z","title":"Bayesian Gaussian Copula Factor Models for Mixed Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1111.0317","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5e842f02df6f72c358f6c589505a056f1e982ccd52a5cc57518a25a9c881bf13","target":"record","created_at":"2026-05-18T03:36:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9ad8f51b0851b5b3fbcfd0c2b51cd0d8b7842857d2688347f4e308cbdd16cc1e","cross_cats_sorted":["stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2011-11-01T21:06:48Z","title_canon_sha256":"73317f03093fbdfbaa5cef1906a757ffd00983792d2302b9fc2ed975c11b26ba"},"schema_version":"1.0","source":{"id":"1111.0317","kind":"arxiv","version":2}},"canonical_sha256":"8cf5e3fad560d046be1bed6a5bc664e18d7ea408603ab54eac78e46ec7210fbb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8cf5e3fad560d046be1bed6a5bc664e18d7ea408603ab54eac78e46ec7210fbb","first_computed_at":"2026-05-18T03:36:46.635531Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:36:46.635531Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yL8USqLl3+QJKqu9pZErH/eliw7vtMcwuXdSzrWzIeKdlffAfz8BRVINhP01DTJAbkezswlfcFhYImPDx0RpAw==","signature_status":"signed_v1","signed_at":"2026-05-18T03:36:46.636247Z","signed_message":"canonical_sha256_bytes"},"source_id":"1111.0317","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5e842f02df6f72c358f6c589505a056f1e982ccd52a5cc57518a25a9c881bf13","sha256:3b0414d8d816d339869ce61ffd4fa664376cadb2c741ebd0a6c38da1cd895145"],"state_sha256":"9c6bd9ca7cf7f31fe49c0576ad6384888ec99446dbd0a32e828623321b12389b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YoS+W5I9sG2GAd04DNBjFDBvezgTm9+YCyg2E535LleaGx6PE3eIXJVlMrGSRMI1x0M3ga8awB8EVukoS8VhCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T03:44:14.570182Z","bundle_sha256":"97e8e85297ff793b313941927529097a686fe48a7a4d9a82ea04be9b8bffd658"}}