{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:QI25KU75TEONZ3Y7QFBR26JB6C","short_pith_number":"pith:QI25KU75","schema_version":"1.0","canonical_sha256":"8235d553fd991cdcef1f81431d7921f0bc9737dcfe4de2b197daea11deb7c982","source":{"kind":"arxiv","id":"1304.2302","version":1},"attestation_state":"computed","paper":{"title":"ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.LG"],"primary_cat":"stat.ML","authors_text":"Dan Lovell, Jonathan Malmaud, Ryan P. Adams, Vikash K. Mansinghka","submitted_at":"2013-04-08T18:34:32Z","abstract_excerpt":"The Dirichlet process (DP) is a fundamental mathematical tool for Bayesian nonparametric modeling, and is widely used in tasks such as density estimation, natural language processing, and time series modeling. Although MCMC inference methods for the DP often provide a gold standard in terms asymptotic accuracy, they can be computationally expensive and are not obviously parallelizable. We propose a reparameterization of the Dirichlet process that induces conditional independencies between the atoms that form the random measure. This conditional independence enables many of the Markov chain tra"},"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":"1304.2302","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-04-08T18:34:32Z","cross_cats_sorted":["cs.DC","cs.LG"],"title_canon_sha256":"847bef8d5c44c80caa54097fc160af12ba9eaca20002cc632dfa1d75db28a975","abstract_canon_sha256":"92916bbe57e5481fe27bfd84904b0babbf926c4229fa0370370e3fc2360621f6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:28:43.474241Z","signature_b64":"1GQT0Ufkd7gqzhJPsGgPj3uVaaB/xN69nrcaU6yTTaYoyHF3gqQ8ElIYx/cf4oui6+OwAs82SVX3nLANj+6CCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8235d553fd991cdcef1f81431d7921f0bc9737dcfe4de2b197daea11deb7c982","last_reissued_at":"2026-05-18T03:28:43.473641Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:28:43.473641Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.LG"],"primary_cat":"stat.ML","authors_text":"Dan Lovell, Jonathan Malmaud, Ryan P. Adams, Vikash K. Mansinghka","submitted_at":"2013-04-08T18:34:32Z","abstract_excerpt":"The Dirichlet process (DP) is a fundamental mathematical tool for Bayesian nonparametric modeling, and is widely used in tasks such as density estimation, natural language processing, and time series modeling. Although MCMC inference methods for the DP often provide a gold standard in terms asymptotic accuracy, they can be computationally expensive and are not obviously parallelizable. We propose a reparameterization of the Dirichlet process that induces conditional independencies between the atoms that form the random measure. This conditional independence enables many of the Markov chain tra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1304.2302","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":"1304.2302","created_at":"2026-05-18T03:28:43.473722+00:00"},{"alias_kind":"arxiv_version","alias_value":"1304.2302v1","created_at":"2026-05-18T03:28:43.473722+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1304.2302","created_at":"2026-05-18T03:28:43.473722+00:00"},{"alias_kind":"pith_short_12","alias_value":"QI25KU75TEON","created_at":"2026-05-18T12:27:57.521954+00:00"},{"alias_kind":"pith_short_16","alias_value":"QI25KU75TEONZ3Y7","created_at":"2026-05-18T12:27:57.521954+00:00"},{"alias_kind":"pith_short_8","alias_value":"QI25KU75","created_at":"2026-05-18T12:27:57.521954+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/QI25KU75TEONZ3Y7QFBR26JB6C","json":"https://pith.science/pith/QI25KU75TEONZ3Y7QFBR26JB6C.json","graph_json":"https://pith.science/api/pith-number/QI25KU75TEONZ3Y7QFBR26JB6C/graph.json","events_json":"https://pith.science/api/pith-number/QI25KU75TEONZ3Y7QFBR26JB6C/events.json","paper":"https://pith.science/paper/QI25KU75"},"agent_actions":{"view_html":"https://pith.science/pith/QI25KU75TEONZ3Y7QFBR26JB6C","download_json":"https://pith.science/pith/QI25KU75TEONZ3Y7QFBR26JB6C.json","view_paper":"https://pith.science/paper/QI25KU75","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1304.2302&json=true","fetch_graph":"https://pith.science/api/pith-number/QI25KU75TEONZ3Y7QFBR26JB6C/graph.json","fetch_events":"https://pith.science/api/pith-number/QI25KU75TEONZ3Y7QFBR26JB6C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QI25KU75TEONZ3Y7QFBR26JB6C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QI25KU75TEONZ3Y7QFBR26JB6C/action/storage_attestation","attest_author":"https://pith.science/pith/QI25KU75TEONZ3Y7QFBR26JB6C/action/author_attestation","sign_citation":"https://pith.science/pith/QI25KU75TEONZ3Y7QFBR26JB6C/action/citation_signature","submit_replication":"https://pith.science/pith/QI25KU75TEONZ3Y7QFBR26JB6C/action/replication_record"}},"created_at":"2026-05-18T03:28:43.473722+00:00","updated_at":"2026-05-18T03:28:43.473722+00:00"}