{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:F4LIS66OXYD77OE3GPN3PGJUN3","short_pith_number":"pith:F4LIS66O","schema_version":"1.0","canonical_sha256":"2f16897bcebe07ffb89b33dbb799346ec7b3091815ce125c5a3d920e9d25c463","source":{"kind":"arxiv","id":"1103.4789","version":3},"attestation_state":"computed","paper":{"title":"The Discrete Infinite Logistic Normal Distribution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Chong Wang, David Blei, John Paisley","submitted_at":"2011-03-24T15:31:47Z","abstract_excerpt":"We present the discrete infinite logistic normal distribution (DILN), a Bayesian nonparametric prior for mixed membership models. DILN is a generalization of the hierarchical Dirichlet process (HDP) that models correlation structure between the weights of the atoms at the group level. We derive a representation of DILN as a normalized collection of gamma-distributed random variables, and study its statistical properties. We consider applications to topic modeling and derive a variational inference algorithm for approximate posterior inference. We study the empirical performance of the DILN top"},"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":"1103.4789","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-03-24T15:31:47Z","cross_cats_sorted":[],"title_canon_sha256":"ebb5af015135b88b5ae355ac6d667eff7a848a36f202e19fbb5418f2242995c7","abstract_canon_sha256":"4cfcc9023d0f57496825c8dbaeb8f374940e5cf7221c9603371d08f5ae864d41"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:22:11.694392Z","signature_b64":"8j5hZQRZAMpKtO7JX3D/6l1NWLph7ApQOSYvr/97PKG6bXoXI2TYYgCmDPiEFbizx/MSq+5E7x5r48ymyeVeBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f16897bcebe07ffb89b33dbb799346ec7b3091815ce125c5a3d920e9d25c463","last_reissued_at":"2026-05-18T02:22:11.693968Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:22:11.693968Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Discrete Infinite Logistic Normal Distribution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Chong Wang, David Blei, John Paisley","submitted_at":"2011-03-24T15:31:47Z","abstract_excerpt":"We present the discrete infinite logistic normal distribution (DILN), a Bayesian nonparametric prior for mixed membership models. DILN is a generalization of the hierarchical Dirichlet process (HDP) that models correlation structure between the weights of the atoms at the group level. We derive a representation of DILN as a normalized collection of gamma-distributed random variables, and study its statistical properties. We consider applications to topic modeling and derive a variational inference algorithm for approximate posterior inference. We study the empirical performance of the DILN top"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1103.4789","kind":"arxiv","version":3},"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":"1103.4789","created_at":"2026-05-18T02:22:11.694026+00:00"},{"alias_kind":"arxiv_version","alias_value":"1103.4789v3","created_at":"2026-05-18T02:22:11.694026+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1103.4789","created_at":"2026-05-18T02:22:11.694026+00:00"},{"alias_kind":"pith_short_12","alias_value":"F4LIS66OXYD7","created_at":"2026-05-18T12:26:28.662955+00:00"},{"alias_kind":"pith_short_16","alias_value":"F4LIS66OXYD77OE3","created_at":"2026-05-18T12:26:28.662955+00:00"},{"alias_kind":"pith_short_8","alias_value":"F4LIS66O","created_at":"2026-05-18T12:26:28.662955+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/F4LIS66OXYD77OE3GPN3PGJUN3","json":"https://pith.science/pith/F4LIS66OXYD77OE3GPN3PGJUN3.json","graph_json":"https://pith.science/api/pith-number/F4LIS66OXYD77OE3GPN3PGJUN3/graph.json","events_json":"https://pith.science/api/pith-number/F4LIS66OXYD77OE3GPN3PGJUN3/events.json","paper":"https://pith.science/paper/F4LIS66O"},"agent_actions":{"view_html":"https://pith.science/pith/F4LIS66OXYD77OE3GPN3PGJUN3","download_json":"https://pith.science/pith/F4LIS66OXYD77OE3GPN3PGJUN3.json","view_paper":"https://pith.science/paper/F4LIS66O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1103.4789&json=true","fetch_graph":"https://pith.science/api/pith-number/F4LIS66OXYD77OE3GPN3PGJUN3/graph.json","fetch_events":"https://pith.science/api/pith-number/F4LIS66OXYD77OE3GPN3PGJUN3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F4LIS66OXYD77OE3GPN3PGJUN3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F4LIS66OXYD77OE3GPN3PGJUN3/action/storage_attestation","attest_author":"https://pith.science/pith/F4LIS66OXYD77OE3GPN3PGJUN3/action/author_attestation","sign_citation":"https://pith.science/pith/F4LIS66OXYD77OE3GPN3PGJUN3/action/citation_signature","submit_replication":"https://pith.science/pith/F4LIS66OXYD77OE3GPN3PGJUN3/action/replication_record"}},"created_at":"2026-05-18T02:22:11.694026+00:00","updated_at":"2026-05-18T02:22:11.694026+00:00"}