{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:C3XNPRYQAWPTY45E5FNXBQA3BA","short_pith_number":"pith:C3XNPRYQ","schema_version":"1.0","canonical_sha256":"16eed7c710059f3c73a4e95b70c01b0801b6d7f730fb57fc73694c74d88ded91","source":{"kind":"arxiv","id":"1905.07499","version":1},"attestation_state":"computed","paper":{"title":"LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ME","stat.ML"],"primary_cat":"stat.CO","authors_text":"Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick","submitted_at":"2019-05-17T22:59:56Z","abstract_excerpt":"Due to the ease of modern data collection, applied statisticians often have access to a large set of covariates that they wish to relate to some observed outcome. Generalized linear models (GLMs) offer a particularly interpretable framework for such an analysis. In these high-dimensional problems, the number of covariates is often large relative to the number of observations, so we face non-trivial inferential uncertainty; a Bayesian approach allows coherent quantification of this uncertainty. Unfortunately, existing methods for Bayesian inference in GLMs require running times roughly cubic in"},"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":"1905.07499","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-05-17T22:59:56Z","cross_cats_sorted":["cs.LG","stat.ME","stat.ML"],"title_canon_sha256":"9a169b39c40706ff9656847af66c193ebd409d366b85ac340f98512b078f9070","abstract_canon_sha256":"15675d4b58e3765f17115bec5bcc4c0a82a50c49cb1ad5175135227f746bc62c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:49.542559Z","signature_b64":"H3P8pN8jyZQUxj5h3DSsniYK0QqwFczvpofwiW78DwPc5TRNO4+FFlcNTLSQi47a5OsRJLbI/cW9T8dRImRwDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"16eed7c710059f3c73a4e95b70c01b0801b6d7f730fb57fc73694c74d88ded91","last_reissued_at":"2026-05-17T23:45:49.541879Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:49.541879Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ME","stat.ML"],"primary_cat":"stat.CO","authors_text":"Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick","submitted_at":"2019-05-17T22:59:56Z","abstract_excerpt":"Due to the ease of modern data collection, applied statisticians often have access to a large set of covariates that they wish to relate to some observed outcome. Generalized linear models (GLMs) offer a particularly interpretable framework for such an analysis. In these high-dimensional problems, the number of covariates is often large relative to the number of observations, so we face non-trivial inferential uncertainty; a Bayesian approach allows coherent quantification of this uncertainty. Unfortunately, existing methods for Bayesian inference in GLMs require running times roughly cubic in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.07499","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":"1905.07499","created_at":"2026-05-17T23:45:49.541974+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.07499v1","created_at":"2026-05-17T23:45:49.541974+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.07499","created_at":"2026-05-17T23:45:49.541974+00:00"},{"alias_kind":"pith_short_12","alias_value":"C3XNPRYQAWPT","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"C3XNPRYQAWPTY45E","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"C3XNPRYQ","created_at":"2026-05-18T12:33:12.712433+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/C3XNPRYQAWPTY45E5FNXBQA3BA","json":"https://pith.science/pith/C3XNPRYQAWPTY45E5FNXBQA3BA.json","graph_json":"https://pith.science/api/pith-number/C3XNPRYQAWPTY45E5FNXBQA3BA/graph.json","events_json":"https://pith.science/api/pith-number/C3XNPRYQAWPTY45E5FNXBQA3BA/events.json","paper":"https://pith.science/paper/C3XNPRYQ"},"agent_actions":{"view_html":"https://pith.science/pith/C3XNPRYQAWPTY45E5FNXBQA3BA","download_json":"https://pith.science/pith/C3XNPRYQAWPTY45E5FNXBQA3BA.json","view_paper":"https://pith.science/paper/C3XNPRYQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.07499&json=true","fetch_graph":"https://pith.science/api/pith-number/C3XNPRYQAWPTY45E5FNXBQA3BA/graph.json","fetch_events":"https://pith.science/api/pith-number/C3XNPRYQAWPTY45E5FNXBQA3BA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C3XNPRYQAWPTY45E5FNXBQA3BA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C3XNPRYQAWPTY45E5FNXBQA3BA/action/storage_attestation","attest_author":"https://pith.science/pith/C3XNPRYQAWPTY45E5FNXBQA3BA/action/author_attestation","sign_citation":"https://pith.science/pith/C3XNPRYQAWPTY45E5FNXBQA3BA/action/citation_signature","submit_replication":"https://pith.science/pith/C3XNPRYQAWPTY45E5FNXBQA3BA/action/replication_record"}},"created_at":"2026-05-17T23:45:49.541974+00:00","updated_at":"2026-05-17T23:45:49.541974+00:00"}