{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:57FXS72LUWJCFWLYSP5EF2K6VT","short_pith_number":"pith:57FXS72L","schema_version":"1.0","canonical_sha256":"efcb797f4ba59222d97893fa42e95eacf26fe7275e7b4e7968604fcf9c575039","source":{"kind":"arxiv","id":"1511.03947","version":1},"attestation_state":"computed","paper":{"title":"Bayesian Analysis of Dynamic Linear Topic Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.ML","authors_text":"Brian Howard, Chris Glynn, David L. Banks, Surya T. Tokdar","submitted_at":"2015-11-12T16:26:13Z","abstract_excerpt":"In dynamic topic modeling, the proportional contribution of a topic to a document depends on the temporal dynamics of that topic's overall prevalence in the corpus. We extend the Dynamic Topic Model of Blei and Lafferty (2006) by explicitly modeling document level topic proportions with covariates and dynamic structure that includes polynomial trends and periodicity. A Markov Chain Monte Carlo (MCMC) algorithm that utilizes Polya-Gamma data augmentation is developed for posterior inference. Conditional independencies in the model and sampling are made explicit, and our MCMC algorithm is parall"},"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":"1511.03947","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-11-12T16:26:13Z","cross_cats_sorted":["cs.LG","stat.ME"],"title_canon_sha256":"e4ed9858789e0a7b719f8d60fa425c521daea55fd887015fad8de52dfa438225","abstract_canon_sha256":"1adb6c4cc5094c8679a646d0994b2caf006d7fcfde04abb01e61f17a77a8f561"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:27:06.391128Z","signature_b64":"7/NCdsQP1wMejc4AjnOYrcNSJodqhtbYUp/ZSSXeWpHdKqZQDL0YXNiKz+OOliT6buG0PHTpYLVBOHaQTjShBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"efcb797f4ba59222d97893fa42e95eacf26fe7275e7b4e7968604fcf9c575039","last_reissued_at":"2026-05-18T01:27:06.390430Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:27:06.390430Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Analysis of Dynamic Linear Topic Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.ML","authors_text":"Brian Howard, Chris Glynn, David L. Banks, Surya T. Tokdar","submitted_at":"2015-11-12T16:26:13Z","abstract_excerpt":"In dynamic topic modeling, the proportional contribution of a topic to a document depends on the temporal dynamics of that topic's overall prevalence in the corpus. We extend the Dynamic Topic Model of Blei and Lafferty (2006) by explicitly modeling document level topic proportions with covariates and dynamic structure that includes polynomial trends and periodicity. A Markov Chain Monte Carlo (MCMC) algorithm that utilizes Polya-Gamma data augmentation is developed for posterior inference. Conditional independencies in the model and sampling are made explicit, and our MCMC algorithm is parall"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.03947","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":"1511.03947","created_at":"2026-05-18T01:27:06.390547+00:00"},{"alias_kind":"arxiv_version","alias_value":"1511.03947v1","created_at":"2026-05-18T01:27:06.390547+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.03947","created_at":"2026-05-18T01:27:06.390547+00:00"},{"alias_kind":"pith_short_12","alias_value":"57FXS72LUWJC","created_at":"2026-05-18T12:29:05.191682+00:00"},{"alias_kind":"pith_short_16","alias_value":"57FXS72LUWJCFWLY","created_at":"2026-05-18T12:29:05.191682+00:00"},{"alias_kind":"pith_short_8","alias_value":"57FXS72L","created_at":"2026-05-18T12:29:05.191682+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/57FXS72LUWJCFWLYSP5EF2K6VT","json":"https://pith.science/pith/57FXS72LUWJCFWLYSP5EF2K6VT.json","graph_json":"https://pith.science/api/pith-number/57FXS72LUWJCFWLYSP5EF2K6VT/graph.json","events_json":"https://pith.science/api/pith-number/57FXS72LUWJCFWLYSP5EF2K6VT/events.json","paper":"https://pith.science/paper/57FXS72L"},"agent_actions":{"view_html":"https://pith.science/pith/57FXS72LUWJCFWLYSP5EF2K6VT","download_json":"https://pith.science/pith/57FXS72LUWJCFWLYSP5EF2K6VT.json","view_paper":"https://pith.science/paper/57FXS72L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1511.03947&json=true","fetch_graph":"https://pith.science/api/pith-number/57FXS72LUWJCFWLYSP5EF2K6VT/graph.json","fetch_events":"https://pith.science/api/pith-number/57FXS72LUWJCFWLYSP5EF2K6VT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/57FXS72LUWJCFWLYSP5EF2K6VT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/57FXS72LUWJCFWLYSP5EF2K6VT/action/storage_attestation","attest_author":"https://pith.science/pith/57FXS72LUWJCFWLYSP5EF2K6VT/action/author_attestation","sign_citation":"https://pith.science/pith/57FXS72LUWJCFWLYSP5EF2K6VT/action/citation_signature","submit_replication":"https://pith.science/pith/57FXS72LUWJCFWLYSP5EF2K6VT/action/replication_record"}},"created_at":"2026-05-18T01:27:06.390547+00:00","updated_at":"2026-05-18T01:27:06.390547+00:00"}