{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:SXB36SALEYTJFJ3FROVFJQ4JQV","short_pith_number":"pith:SXB36SAL","schema_version":"1.0","canonical_sha256":"95c3bf480b262692a7658baa54c38985560c56d6681ec2001f7b5bb263e2b88f","source":{"kind":"arxiv","id":"1411.6370","version":2},"attestation_state":"computed","paper":{"title":"Big Learning with Bayesian Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.CO","stat.ME","stat.ML"],"primary_cat":"cs.LG","authors_text":"Bo Zhang, Jianfei Chen, Jun Zhu, Wenbo Hu","submitted_at":"2014-11-24T07:28:51Z","abstract_excerpt":"Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems, and applications with Big Data. Bayesian methods represent one important class of statistic methods for machine learning, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This article provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning, including nonparametric Bayesian methods for adaptively inferr"},"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":"1411.6370","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-11-24T07:28:51Z","cross_cats_sorted":["stat.AP","stat.CO","stat.ME","stat.ML"],"title_canon_sha256":"af6a9a085157f1f43705b9cdbbbaa07a57fa6338dc91a03d6c81517af520215c","abstract_canon_sha256":"05c290386be46fed620fe3bf802490b0f0fcb3f89907bdcaad3e461bc14e457c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:49:47.407465Z","signature_b64":"GMu7isbE0AdbRh6vb2Fik2NBcR1/2ULPEMXb063gljK0plb9hPa6qLStzydW1wSux23ACuUsASHsuwZ8t55XCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"95c3bf480b262692a7658baa54c38985560c56d6681ec2001f7b5bb263e2b88f","last_reissued_at":"2026-05-18T00:49:47.406776Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:49:47.406776Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Big Learning with Bayesian Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.CO","stat.ME","stat.ML"],"primary_cat":"cs.LG","authors_text":"Bo Zhang, Jianfei Chen, Jun Zhu, Wenbo Hu","submitted_at":"2014-11-24T07:28:51Z","abstract_excerpt":"Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems, and applications with Big Data. Bayesian methods represent one important class of statistic methods for machine learning, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This article provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning, including nonparametric Bayesian methods for adaptively inferr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1411.6370","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1411.6370","created_at":"2026-05-18T00:49:47.406881+00:00"},{"alias_kind":"arxiv_version","alias_value":"1411.6370v2","created_at":"2026-05-18T00:49:47.406881+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1411.6370","created_at":"2026-05-18T00:49:47.406881+00:00"},{"alias_kind":"pith_short_12","alias_value":"SXB36SALEYTJ","created_at":"2026-05-18T12:28:49.207871+00:00"},{"alias_kind":"pith_short_16","alias_value":"SXB36SALEYTJFJ3F","created_at":"2026-05-18T12:28:49.207871+00:00"},{"alias_kind":"pith_short_8","alias_value":"SXB36SAL","created_at":"2026-05-18T12:28:49.207871+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/SXB36SALEYTJFJ3FROVFJQ4JQV","json":"https://pith.science/pith/SXB36SALEYTJFJ3FROVFJQ4JQV.json","graph_json":"https://pith.science/api/pith-number/SXB36SALEYTJFJ3FROVFJQ4JQV/graph.json","events_json":"https://pith.science/api/pith-number/SXB36SALEYTJFJ3FROVFJQ4JQV/events.json","paper":"https://pith.science/paper/SXB36SAL"},"agent_actions":{"view_html":"https://pith.science/pith/SXB36SALEYTJFJ3FROVFJQ4JQV","download_json":"https://pith.science/pith/SXB36SALEYTJFJ3FROVFJQ4JQV.json","view_paper":"https://pith.science/paper/SXB36SAL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1411.6370&json=true","fetch_graph":"https://pith.science/api/pith-number/SXB36SALEYTJFJ3FROVFJQ4JQV/graph.json","fetch_events":"https://pith.science/api/pith-number/SXB36SALEYTJFJ3FROVFJQ4JQV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SXB36SALEYTJFJ3FROVFJQ4JQV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SXB36SALEYTJFJ3FROVFJQ4JQV/action/storage_attestation","attest_author":"https://pith.science/pith/SXB36SALEYTJFJ3FROVFJQ4JQV/action/author_attestation","sign_citation":"https://pith.science/pith/SXB36SALEYTJFJ3FROVFJQ4JQV/action/citation_signature","submit_replication":"https://pith.science/pith/SXB36SALEYTJFJ3FROVFJQ4JQV/action/replication_record"}},"created_at":"2026-05-18T00:49:47.406881+00:00","updated_at":"2026-05-18T00:49:47.406881+00:00"}