{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:G2HA7ENDSMUKT7IOFEFMT23K7A","short_pith_number":"pith:G2HA7END","canonical_record":{"source":{"id":"1612.02875","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-12-09T00:42:47Z","cross_cats_sorted":[],"title_canon_sha256":"ed8ade40c940d16e2b4b8c8590762092ee36d61152bf9f1c9e9989051a1bcc94","abstract_canon_sha256":"0e75472703d61acdb6219cc6a9551bcf4e734470ae54a78c9b7380ffcf7f643e"},"schema_version":"1.0"},"canonical_sha256":"368e0f91a39328a9fd0e290ac9eb6af81954a43f5e15cfc438b88af5c25df3d8","source":{"kind":"arxiv","id":"1612.02875","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.02875","created_at":"2026-05-18T00:53:47Z"},{"alias_kind":"arxiv_version","alias_value":"1612.02875v2","created_at":"2026-05-18T00:53:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.02875","created_at":"2026-05-18T00:53:47Z"},{"alias_kind":"pith_short_12","alias_value":"G2HA7ENDSMUK","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_16","alias_value":"G2HA7ENDSMUKT7IO","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_8","alias_value":"G2HA7END","created_at":"2026-05-18T12:30:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:G2HA7ENDSMUKT7IOFEFMT23K7A","target":"record","payload":{"canonical_record":{"source":{"id":"1612.02875","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-12-09T00:42:47Z","cross_cats_sorted":[],"title_canon_sha256":"ed8ade40c940d16e2b4b8c8590762092ee36d61152bf9f1c9e9989051a1bcc94","abstract_canon_sha256":"0e75472703d61acdb6219cc6a9551bcf4e734470ae54a78c9b7380ffcf7f643e"},"schema_version":"1.0"},"canonical_sha256":"368e0f91a39328a9fd0e290ac9eb6af81954a43f5e15cfc438b88af5c25df3d8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:53:47.508895Z","signature_b64":"/LHi3UcUgnU60/eVSDYXW7lVcuCy/Tvn4/VLa4dSUFhXEb/sjK+7qaI0OfbUqRAzfFJWkHR9XksGAIFhTHS+DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"368e0f91a39328a9fd0e290ac9eb6af81954a43f5e15cfc438b88af5c25df3d8","last_reissued_at":"2026-05-18T00:53:47.508516Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:53:47.508516Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1612.02875","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":"2026-05-18T00:53:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Nm3iDGeFYicwh9eUbNwtSh5SkXrm21ECYtnVjszSfbyV4+C9M5KXQ9QF9gFDF3+Q2Vv49ymn0n/hovCe9OyQAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T18:50:56.302136Z"},"content_sha256":"194450197fac1af9118b5e6bd097cdfa7a8fd9d3e639111c697a060f62b58569","schema_version":"1.0","event_id":"sha256:194450197fac1af9118b5e6bd097cdfa7a8fd9d3e639111c697a060f62b58569"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:G2HA7ENDSMUKT7IOFEFMT23K7A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Divide and Conquer Strategy for High Dimensional Bayesian Factor Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Barbara Engelhardt, Debdeep Pati, Gautam Sabnis, Natesh Pillai","submitted_at":"2016-12-09T00:42:47Z","abstract_excerpt":"We propose a distributed computing framework, based on a divide and conquer strategy and hierarchical modeling, to accelerate posterior inference for high-dimensional Bayesian factor models. Our approach distributes the task of high-dimensional covariance matrix estimation to multiple cores, solves each subproblem separately via a latent factor model, and then combines these estimates to produce a global estimate of the covariance matrix. Existing divide and conquer methods focus exclusively on dividing the total number of observations $n$ into subsamples while keeping the dimension $p$ fixed."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.02875","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":"2026-05-18T00:53:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FFLhmfPpanfEo1zR7BM07rlRIc2BcKl7kO3D2P1ht95+gCkF+1Sq+13Lt1h5UI29o9kQjZU+hgZdfgKNU/lSDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T18:50:56.302547Z"},"content_sha256":"557f564e1d1f582cdd99709fe748f6eb3408f59df52eaa1c1e5db421cfa3d576","schema_version":"1.0","event_id":"sha256:557f564e1d1f582cdd99709fe748f6eb3408f59df52eaa1c1e5db421cfa3d576"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/G2HA7ENDSMUKT7IOFEFMT23K7A/bundle.json","state_url":"https://pith.science/pith/G2HA7ENDSMUKT7IOFEFMT23K7A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/G2HA7ENDSMUKT7IOFEFMT23K7A/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":"2026-06-06T18:50:56Z","links":{"resolver":"https://pith.science/pith/G2HA7ENDSMUKT7IOFEFMT23K7A","bundle":"https://pith.science/pith/G2HA7ENDSMUKT7IOFEFMT23K7A/bundle.json","state":"https://pith.science/pith/G2HA7ENDSMUKT7IOFEFMT23K7A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/G2HA7ENDSMUKT7IOFEFMT23K7A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:G2HA7ENDSMUKT7IOFEFMT23K7A","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":"0e75472703d61acdb6219cc6a9551bcf4e734470ae54a78c9b7380ffcf7f643e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-12-09T00:42:47Z","title_canon_sha256":"ed8ade40c940d16e2b4b8c8590762092ee36d61152bf9f1c9e9989051a1bcc94"},"schema_version":"1.0","source":{"id":"1612.02875","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.02875","created_at":"2026-05-18T00:53:47Z"},{"alias_kind":"arxiv_version","alias_value":"1612.02875v2","created_at":"2026-05-18T00:53:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.02875","created_at":"2026-05-18T00:53:47Z"},{"alias_kind":"pith_short_12","alias_value":"G2HA7ENDSMUK","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_16","alias_value":"G2HA7ENDSMUKT7IO","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_8","alias_value":"G2HA7END","created_at":"2026-05-18T12:30:15Z"}],"graph_snapshots":[{"event_id":"sha256:557f564e1d1f582cdd99709fe748f6eb3408f59df52eaa1c1e5db421cfa3d576","target":"graph","created_at":"2026-05-18T00:53:47Z","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 a distributed computing framework, based on a divide and conquer strategy and hierarchical modeling, to accelerate posterior inference for high-dimensional Bayesian factor models. Our approach distributes the task of high-dimensional covariance matrix estimation to multiple cores, solves each subproblem separately via a latent factor model, and then combines these estimates to produce a global estimate of the covariance matrix. Existing divide and conquer methods focus exclusively on dividing the total number of observations $n$ into subsamples while keeping the dimension $p$ fixed.","authors_text":"Barbara Engelhardt, Debdeep Pati, Gautam Sabnis, Natesh Pillai","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-12-09T00:42:47Z","title":"A Divide and Conquer Strategy for High Dimensional Bayesian Factor Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.02875","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":"sha256:194450197fac1af9118b5e6bd097cdfa7a8fd9d3e639111c697a060f62b58569","target":"record","created_at":"2026-05-18T00:53:47Z","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":"0e75472703d61acdb6219cc6a9551bcf4e734470ae54a78c9b7380ffcf7f643e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-12-09T00:42:47Z","title_canon_sha256":"ed8ade40c940d16e2b4b8c8590762092ee36d61152bf9f1c9e9989051a1bcc94"},"schema_version":"1.0","source":{"id":"1612.02875","kind":"arxiv","version":2}},"canonical_sha256":"368e0f91a39328a9fd0e290ac9eb6af81954a43f5e15cfc438b88af5c25df3d8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"368e0f91a39328a9fd0e290ac9eb6af81954a43f5e15cfc438b88af5c25df3d8","first_computed_at":"2026-05-18T00:53:47.508516Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:53:47.508516Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/LHi3UcUgnU60/eVSDYXW7lVcuCy/Tvn4/VLa4dSUFhXEb/sjK+7qaI0OfbUqRAzfFJWkHR9XksGAIFhTHS+DA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:53:47.508895Z","signed_message":"canonical_sha256_bytes"},"source_id":"1612.02875","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:194450197fac1af9118b5e6bd097cdfa7a8fd9d3e639111c697a060f62b58569","sha256:557f564e1d1f582cdd99709fe748f6eb3408f59df52eaa1c1e5db421cfa3d576"],"state_sha256":"07785493f815205b2f438598d9919058628588f5efcc999104355a2fb12ebdf0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dFRGl6jVjgK3XvBMi5tle3cIp2V97J4o6XgXbUvehqCeubeHHNjvf+2PiV2mbafpdmi2Fsgfe7iPVG7+HfuLCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T18:50:56.305182Z","bundle_sha256":"b1a63f5de9a4e3db467401a8180472fd51ca0e5f47f3dac3ce007bdd4fe7ed37"}}