{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:M5XVY4JIQQ5IGIG63U3MNXWWS6","short_pith_number":"pith:M5XVY4JI","canonical_record":{"source":{"id":"1507.00824","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-07-03T06:14:26Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"29553351a7f70c6cf420ac66b1e536ffb6274ae85be0e13e7866e0b195720858","abstract_canon_sha256":"d5a4e57a3bb4dd3de15ab90328aa1543bc8acb45b45f34ddd13537c419083a33"},"schema_version":"1.0"},"canonical_sha256":"676f5c7128843a8320dedd36c6ded6979876edbcffd1ca9466ab5eb512e32e60","source":{"kind":"arxiv","id":"1507.00824","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1507.00824","created_at":"2026-05-18T01:37:22Z"},{"alias_kind":"arxiv_version","alias_value":"1507.00824v1","created_at":"2026-05-18T01:37:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.00824","created_at":"2026-05-18T01:37:22Z"},{"alias_kind":"pith_short_12","alias_value":"M5XVY4JIQQ5I","created_at":"2026-05-18T12:29:32Z"},{"alias_kind":"pith_short_16","alias_value":"M5XVY4JIQQ5IGIG6","created_at":"2026-05-18T12:29:32Z"},{"alias_kind":"pith_short_8","alias_value":"M5XVY4JI","created_at":"2026-05-18T12:29:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:M5XVY4JIQQ5IGIG63U3MNXWWS6","target":"record","payload":{"canonical_record":{"source":{"id":"1507.00824","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-07-03T06:14:26Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"29553351a7f70c6cf420ac66b1e536ffb6274ae85be0e13e7866e0b195720858","abstract_canon_sha256":"d5a4e57a3bb4dd3de15ab90328aa1543bc8acb45b45f34ddd13537c419083a33"},"schema_version":"1.0"},"canonical_sha256":"676f5c7128843a8320dedd36c6ded6979876edbcffd1ca9466ab5eb512e32e60","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:37:22.570667Z","signature_b64":"o/JGvFbr0Pwvbbqf2L/tl1HWV0mfkYwRxvB/z3m8ylTcHQ0B4f5FSszWXYxeimqSJ2AzmjerbQninWtF5xa7Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"676f5c7128843a8320dedd36c6ded6979876edbcffd1ca9466ab5eb512e32e60","last_reissued_at":"2026-05-18T01:37:22.569977Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:37:22.569977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1507.00824","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:37:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R3RljEWwsW4TeAn9y2OCNBX974yrcUCIhtP8fyOr24X0xeXdKFqfl/Wne/qDonU2bqfuQ+jLqyYqvmY4Z+mxBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T22:04:32.774936Z"},"content_sha256":"b93568399ce2717f41b8f121102cb67111192baf974a5dbcb5cc458686fb4b79","schema_version":"1.0","event_id":"sha256:b93568399ce2717f41b8f121102cb67111192baf974a5dbcb5cc458686fb4b79"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:M5XVY4JIQQ5IGIG63U3MNXWWS6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"D-MFVI: Distributed Mean Field Variational Inference using Bregman ADMM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Behnam Babagholami-Mohamadabadi, Sejong Yoon, Vladimir Pavlovic","submitted_at":"2015-07-03T06:14:26Z","abstract_excerpt":"Bayesian models provide a framework for probabilistic modelling of complex datasets. However, many of such models are computationally demanding especially in the presence of large datasets. On the other hand, in sensor network applications, statistical (Bayesian) parameter estimation usually needs distributed algorithms, in which both data and computation are distributed across the nodes of the network. In this paper we propose a general framework for distributed Bayesian learning using Bregman Alternating Direction Method of Multipliers (B-ADMM). We demonstrate the utility of our framework, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.00824","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:37:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wa347DCX6UGxr+7npWFPhWL9p6jbwfD7DQd/8599N2gsVlvwiKlON7kQ+R3dQxWLGiJFt/DqGYUvDV78/sfECA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T22:04:32.775278Z"},"content_sha256":"cb1b2fbec108a941a57a28309c000c732b115ee12e9bea5cc1f12c4b533d9f36","schema_version":"1.0","event_id":"sha256:cb1b2fbec108a941a57a28309c000c732b115ee12e9bea5cc1f12c4b533d9f36"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/M5XVY4JIQQ5IGIG63U3MNXWWS6/bundle.json","state_url":"https://pith.science/pith/M5XVY4JIQQ5IGIG63U3MNXWWS6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/M5XVY4JIQQ5IGIG63U3MNXWWS6/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-02T22:04:32Z","links":{"resolver":"https://pith.science/pith/M5XVY4JIQQ5IGIG63U3MNXWWS6","bundle":"https://pith.science/pith/M5XVY4JIQQ5IGIG63U3MNXWWS6/bundle.json","state":"https://pith.science/pith/M5XVY4JIQQ5IGIG63U3MNXWWS6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/M5XVY4JIQQ5IGIG63U3MNXWWS6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:M5XVY4JIQQ5IGIG63U3MNXWWS6","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d5a4e57a3bb4dd3de15ab90328aa1543bc8acb45b45f34ddd13537c419083a33","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-07-03T06:14:26Z","title_canon_sha256":"29553351a7f70c6cf420ac66b1e536ffb6274ae85be0e13e7866e0b195720858"},"schema_version":"1.0","source":{"id":"1507.00824","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1507.00824","created_at":"2026-05-18T01:37:22Z"},{"alias_kind":"arxiv_version","alias_value":"1507.00824v1","created_at":"2026-05-18T01:37:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.00824","created_at":"2026-05-18T01:37:22Z"},{"alias_kind":"pith_short_12","alias_value":"M5XVY4JIQQ5I","created_at":"2026-05-18T12:29:32Z"},{"alias_kind":"pith_short_16","alias_value":"M5XVY4JIQQ5IGIG6","created_at":"2026-05-18T12:29:32Z"},{"alias_kind":"pith_short_8","alias_value":"M5XVY4JI","created_at":"2026-05-18T12:29:32Z"}],"graph_snapshots":[{"event_id":"sha256:cb1b2fbec108a941a57a28309c000c732b115ee12e9bea5cc1f12c4b533d9f36","target":"graph","created_at":"2026-05-18T01:37:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Bayesian models provide a framework for probabilistic modelling of complex datasets. However, many of such models are computationally demanding especially in the presence of large datasets. On the other hand, in sensor network applications, statistical (Bayesian) parameter estimation usually needs distributed algorithms, in which both data and computation are distributed across the nodes of the network. In this paper we propose a general framework for distributed Bayesian learning using Bregman Alternating Direction Method of Multipliers (B-ADMM). We demonstrate the utility of our framework, w","authors_text":"Behnam Babagholami-Mohamadabadi, Sejong Yoon, Vladimir Pavlovic","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-07-03T06:14:26Z","title":"D-MFVI: Distributed Mean Field Variational Inference using Bregman ADMM"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.00824","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b93568399ce2717f41b8f121102cb67111192baf974a5dbcb5cc458686fb4b79","target":"record","created_at":"2026-05-18T01:37:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d5a4e57a3bb4dd3de15ab90328aa1543bc8acb45b45f34ddd13537c419083a33","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-07-03T06:14:26Z","title_canon_sha256":"29553351a7f70c6cf420ac66b1e536ffb6274ae85be0e13e7866e0b195720858"},"schema_version":"1.0","source":{"id":"1507.00824","kind":"arxiv","version":1}},"canonical_sha256":"676f5c7128843a8320dedd36c6ded6979876edbcffd1ca9466ab5eb512e32e60","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"676f5c7128843a8320dedd36c6ded6979876edbcffd1ca9466ab5eb512e32e60","first_computed_at":"2026-05-18T01:37:22.569977Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:37:22.569977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"o/JGvFbr0Pwvbbqf2L/tl1HWV0mfkYwRxvB/z3m8ylTcHQ0B4f5FSszWXYxeimqSJ2AzmjerbQninWtF5xa7Dw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:37:22.570667Z","signed_message":"canonical_sha256_bytes"},"source_id":"1507.00824","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b93568399ce2717f41b8f121102cb67111192baf974a5dbcb5cc458686fb4b79","sha256:cb1b2fbec108a941a57a28309c000c732b115ee12e9bea5cc1f12c4b533d9f36"],"state_sha256":"27a0fbaf910d294460a9f426794c80ef2b5e7bfec1aa81be05d7bde6d0d552ad"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y/w1+4z9uA6DJE0ET+kVdz9DOPkkaKmaKlYmCL9JxQuhxE5+SOBqlFvd4mJelsnRUW25fxTpmISPCNyy78QsBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T22:04:32.777285Z","bundle_sha256":"208a1bf15d4febe81e9ad18ec2067ea2fecabc442146649ae187d3db3833472f"}}