{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:35VTBHNPGNWTIBGAY7KLVVQCML","short_pith_number":"pith:35VTBHNP","canonical_record":{"source":{"id":"1511.04656","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-11-15T04:01:44Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"bf2b7961dd51ff46951f7d7a28a0aa4f52e02d54ba42f505d33238bf73680ca7","abstract_canon_sha256":"9cbed63eb8048b7260a11acae34655b397ce28fb274f9ca6943b6a32155cd30e"},"schema_version":"1.0"},"canonical_sha256":"df6b309daf336d3404c0c7d4bad60262fc8e32edf88659af33da7e27e520e041","source":{"kind":"arxiv","id":"1511.04656","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.04656","created_at":"2026-05-18T01:26:52Z"},{"alias_kind":"arxiv_version","alias_value":"1511.04656v1","created_at":"2026-05-18T01:26:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.04656","created_at":"2026-05-18T01:26:52Z"},{"alias_kind":"pith_short_12","alias_value":"35VTBHNPGNWT","created_at":"2026-05-18T12:29:02Z"},{"alias_kind":"pith_short_16","alias_value":"35VTBHNPGNWTIBGA","created_at":"2026-05-18T12:29:02Z"},{"alias_kind":"pith_short_8","alias_value":"35VTBHNP","created_at":"2026-05-18T12:29:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:35VTBHNPGNWTIBGAY7KLVVQCML","target":"record","payload":{"canonical_record":{"source":{"id":"1511.04656","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-11-15T04:01:44Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"bf2b7961dd51ff46951f7d7a28a0aa4f52e02d54ba42f505d33238bf73680ca7","abstract_canon_sha256":"9cbed63eb8048b7260a11acae34655b397ce28fb274f9ca6943b6a32155cd30e"},"schema_version":"1.0"},"canonical_sha256":"df6b309daf336d3404c0c7d4bad60262fc8e32edf88659af33da7e27e520e041","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:26:52.111070Z","signature_b64":"YYfyP3WsIikJvPLcbiWVi8cknJDcwgzvA8Q8ywZ/TDvbyHHNpBvdSEyr2Jm49nLyG+FmF7CoCLeLPxIZkMBeDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df6b309daf336d3404c0c7d4bad60262fc8e32edf88659af33da7e27e520e041","last_reissued_at":"2026-05-18T01:26:52.110367Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:26:52.110367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1511.04656","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:26:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N3Dcd1Bw/AmHIoW68sxYeXN8hB2EoFMlCn1AxIjBxg53j4J/08r1J3ApK21Yb7+Xj24xaGwkZbW8+J0WHNTTCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T20:14:45.861341Z"},"content_sha256":"f13934d5c8c1d8859f078abe028731426bc55037f9b930a973c90006da9508b3","schema_version":"1.0","event_id":"sha256:f13934d5c8c1d8859f078abe028731426bc55037f9b930a973c90006da9508b3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:35VTBHNPGNWTIBGAY7KLVVQCML","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mixed and missing data: a unified treatment with latent graphical models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Jinzhu Jia, Xiao Li, Yuan Yao","submitted_at":"2015-11-15T04:01:44Z","abstract_excerpt":"We propose to learn latent graphical models when data have mixed variables and missing values. This model could be used for further data analysis, including regression, classification, ranking etc. It also could be used for imputing missing values. We specify a latent Gaussian model for the data, where the categorical variables are generated by discretizing an unobserved variable and the latent variables are multivariate Gaussian. The observed data consists of two parts: observed Gaussian variables and observed categorical variables, where the latter part is considered as partially missing Gau"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.04656","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:26:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZDm/Bcdot2cDyDi1RSPVDAT6ShkoG4KeJk/cOByelFizcpxcQOlSDnIaaUhUgGN6mXOTHQJ5q2hOkw2IvXkqAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T20:14:45.861976Z"},"content_sha256":"8d1fdcd752a91ef0f2abc5102e852033150090f7fcda9334b4467b3355047465","schema_version":"1.0","event_id":"sha256:8d1fdcd752a91ef0f2abc5102e852033150090f7fcda9334b4467b3355047465"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/35VTBHNPGNWTIBGAY7KLVVQCML/bundle.json","state_url":"https://pith.science/pith/35VTBHNPGNWTIBGAY7KLVVQCML/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/35VTBHNPGNWTIBGAY7KLVVQCML/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":"2026-06-06T20:14:45Z","links":{"resolver":"https://pith.science/pith/35VTBHNPGNWTIBGAY7KLVVQCML","bundle":"https://pith.science/pith/35VTBHNPGNWTIBGAY7KLVVQCML/bundle.json","state":"https://pith.science/pith/35VTBHNPGNWTIBGAY7KLVVQCML/state.json","well_known_bundle":"https://pith.science/.well-known/pith/35VTBHNPGNWTIBGAY7KLVVQCML/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:35VTBHNPGNWTIBGAY7KLVVQCML","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":"9cbed63eb8048b7260a11acae34655b397ce28fb274f9ca6943b6a32155cd30e","cross_cats_sorted":["stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-11-15T04:01:44Z","title_canon_sha256":"bf2b7961dd51ff46951f7d7a28a0aa4f52e02d54ba42f505d33238bf73680ca7"},"schema_version":"1.0","source":{"id":"1511.04656","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.04656","created_at":"2026-05-18T01:26:52Z"},{"alias_kind":"arxiv_version","alias_value":"1511.04656v1","created_at":"2026-05-18T01:26:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.04656","created_at":"2026-05-18T01:26:52Z"},{"alias_kind":"pith_short_12","alias_value":"35VTBHNPGNWT","created_at":"2026-05-18T12:29:02Z"},{"alias_kind":"pith_short_16","alias_value":"35VTBHNPGNWTIBGA","created_at":"2026-05-18T12:29:02Z"},{"alias_kind":"pith_short_8","alias_value":"35VTBHNP","created_at":"2026-05-18T12:29:02Z"}],"graph_snapshots":[{"event_id":"sha256:8d1fdcd752a91ef0f2abc5102e852033150090f7fcda9334b4467b3355047465","target":"graph","created_at":"2026-05-18T01:26:52Z","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":"We propose to learn latent graphical models when data have mixed variables and missing values. This model could be used for further data analysis, including regression, classification, ranking etc. It also could be used for imputing missing values. We specify a latent Gaussian model for the data, where the categorical variables are generated by discretizing an unobserved variable and the latent variables are multivariate Gaussian. The observed data consists of two parts: observed Gaussian variables and observed categorical variables, where the latter part is considered as partially missing Gau","authors_text":"Jinzhu Jia, Xiao Li, Yuan Yao","cross_cats":["stat.AP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-11-15T04:01:44Z","title":"Mixed and missing data: a unified treatment with latent graphical models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.04656","kind":"arxiv","version":1},"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:f13934d5c8c1d8859f078abe028731426bc55037f9b930a973c90006da9508b3","target":"record","created_at":"2026-05-18T01:26:52Z","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":"9cbed63eb8048b7260a11acae34655b397ce28fb274f9ca6943b6a32155cd30e","cross_cats_sorted":["stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-11-15T04:01:44Z","title_canon_sha256":"bf2b7961dd51ff46951f7d7a28a0aa4f52e02d54ba42f505d33238bf73680ca7"},"schema_version":"1.0","source":{"id":"1511.04656","kind":"arxiv","version":1}},"canonical_sha256":"df6b309daf336d3404c0c7d4bad60262fc8e32edf88659af33da7e27e520e041","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"df6b309daf336d3404c0c7d4bad60262fc8e32edf88659af33da7e27e520e041","first_computed_at":"2026-05-18T01:26:52.110367Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:26:52.110367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YYfyP3WsIikJvPLcbiWVi8cknJDcwgzvA8Q8ywZ/TDvbyHHNpBvdSEyr2Jm49nLyG+FmF7CoCLeLPxIZkMBeDA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:26:52.111070Z","signed_message":"canonical_sha256_bytes"},"source_id":"1511.04656","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f13934d5c8c1d8859f078abe028731426bc55037f9b930a973c90006da9508b3","sha256:8d1fdcd752a91ef0f2abc5102e852033150090f7fcda9334b4467b3355047465"],"state_sha256":"5a7b74c26270597e6fd78ec3ce1b361ccd9185bc30c2fbd9b037f65fd262674f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TClmuOOBQ2H+COSE8aOq1OCqWFkcqrM7OesUtdq4GMmFTGb6KNIA4KD20o4auyoNktn6LvyDswImFvvpM0fRBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T20:14:45.865334Z","bundle_sha256":"70ab431b7de58b175ed76c0b9f2003d8b8738d6667516edac328178f5379eed5"}}