{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:2U6ED322C3R372OEFWFO46ZO43","short_pith_number":"pith:2U6ED322","canonical_record":{"source":{"id":"1810.09098","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-22T05:53:22Z","cross_cats_sorted":["cs.LG","stat.CO"],"title_canon_sha256":"c6300d6d6e5c70034cffa8686ffe7f6ffdf1e806fdba49307840e1dd4360bee1","abstract_canon_sha256":"4b13ecd05f73095223f9c6efeca8c155849f3439a6a02acc6f5a9dba67e7c9b5"},"schema_version":"1.0"},"canonical_sha256":"d53c41ef5a16e3bfe9c42d8aee7b2ee6df9ed407214c5d914008d53180e42622","source":{"kind":"arxiv","id":"1810.09098","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.09098","created_at":"2026-05-17T23:41:02Z"},{"alias_kind":"arxiv_version","alias_value":"1810.09098v2","created_at":"2026-05-17T23:41:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.09098","created_at":"2026-05-17T23:41:02Z"},{"alias_kind":"pith_short_12","alias_value":"2U6ED322C3R3","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"2U6ED322C3R372OE","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"2U6ED322","created_at":"2026-05-18T12:32:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:2U6ED322C3R372OEFWFO46ZO43","target":"record","payload":{"canonical_record":{"source":{"id":"1810.09098","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-22T05:53:22Z","cross_cats_sorted":["cs.LG","stat.CO"],"title_canon_sha256":"c6300d6d6e5c70034cffa8686ffe7f6ffdf1e806fdba49307840e1dd4360bee1","abstract_canon_sha256":"4b13ecd05f73095223f9c6efeca8c155849f3439a6a02acc6f5a9dba67e7c9b5"},"schema_version":"1.0"},"canonical_sha256":"d53c41ef5a16e3bfe9c42d8aee7b2ee6df9ed407214c5d914008d53180e42622","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:02.288027Z","signature_b64":"vyNVF0Z4Qv6GTs1gYVlh5UeE2acA6Q/dSmZSmdbVS5jOpsEiGCLu3fSlLDO5e/WZKA4yF0J7+k4m+1T5h49VAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d53c41ef5a16e3bfe9c42d8aee7b2ee6df9ed407214c5d914008d53180e42622","last_reissued_at":"2026-05-17T23:41:02.287546Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:02.287546Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.09098","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:41:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lZfOhvU3PGEIbO7AUbkvQXUHRfz5D2PC61AJfSlN+54u7fRPAXaHIPK60u9nr/CkoMI0FqNt6+RJ8iqR45RzBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T19:37:48.557172Z"},"content_sha256":"5f67fc971236ed59230bb4e8802100fc8ecbbfc8da85b548db8c98a04ea1bb1e","schema_version":"1.0","event_id":"sha256:5f67fc971236ed59230bb4e8802100fc8ecbbfc8da85b548db8c98a04ea1bb1e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:2U6ED322C3R372OEFWFO46ZO43","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Stochastic Gradient MCMC for State Space Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.CO"],"primary_cat":"stat.ML","authors_text":"Christopher Aicher, Emily B. Fox, Nicholas J. Foti, Yi-An Ma","submitted_at":"2018-10-22T05:53:22Z","abstract_excerpt":"State space models (SSMs) are a flexible approach to modeling complex time series. However, inference in SSMs is often computationally prohibitive for long time series. Stochastic gradient MCMC (SGMCMC) is a popular method for scalable Bayesian inference for large independent data. Unfortunately when applied to dependent data, such as in SSMs, SGMCMC's stochastic gradient estimates are biased as they break crucial temporal dependencies. To alleviate this, we propose stochastic gradient estimators that control this bias by performing additional computation in a `buffer' to reduce breaking depen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.09098","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:41:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yL8clUiYqmfC2L9yd/2BzWuII5i9y3Pm+KyGOZjIuHQfxvdBAV9nEoq82BdjI8On2a5JiO+YEibd1bds/uZ7Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T19:37:48.557533Z"},"content_sha256":"5dd50837ac85c06b9ae5ac0598d426587a392c4976010b9244ab4878ab61cb0a","schema_version":"1.0","event_id":"sha256:5dd50837ac85c06b9ae5ac0598d426587a392c4976010b9244ab4878ab61cb0a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2U6ED322C3R372OEFWFO46ZO43/bundle.json","state_url":"https://pith.science/pith/2U6ED322C3R372OEFWFO46ZO43/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2U6ED322C3R372OEFWFO46ZO43/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":"2026-06-01T19:37:48Z","links":{"resolver":"https://pith.science/pith/2U6ED322C3R372OEFWFO46ZO43","bundle":"https://pith.science/pith/2U6ED322C3R372OEFWFO46ZO43/bundle.json","state":"https://pith.science/pith/2U6ED322C3R372OEFWFO46ZO43/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2U6ED322C3R372OEFWFO46ZO43/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:2U6ED322C3R372OEFWFO46ZO43","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":"4b13ecd05f73095223f9c6efeca8c155849f3439a6a02acc6f5a9dba67e7c9b5","cross_cats_sorted":["cs.LG","stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-22T05:53:22Z","title_canon_sha256":"c6300d6d6e5c70034cffa8686ffe7f6ffdf1e806fdba49307840e1dd4360bee1"},"schema_version":"1.0","source":{"id":"1810.09098","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.09098","created_at":"2026-05-17T23:41:02Z"},{"alias_kind":"arxiv_version","alias_value":"1810.09098v2","created_at":"2026-05-17T23:41:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.09098","created_at":"2026-05-17T23:41:02Z"},{"alias_kind":"pith_short_12","alias_value":"2U6ED322C3R3","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"2U6ED322C3R372OE","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"2U6ED322","created_at":"2026-05-18T12:32:02Z"}],"graph_snapshots":[{"event_id":"sha256:5dd50837ac85c06b9ae5ac0598d426587a392c4976010b9244ab4878ab61cb0a","target":"graph","created_at":"2026-05-17T23:41:02Z","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":"State space models (SSMs) are a flexible approach to modeling complex time series. However, inference in SSMs is often computationally prohibitive for long time series. Stochastic gradient MCMC (SGMCMC) is a popular method for scalable Bayesian inference for large independent data. Unfortunately when applied to dependent data, such as in SSMs, SGMCMC's stochastic gradient estimates are biased as they break crucial temporal dependencies. To alleviate this, we propose stochastic gradient estimators that control this bias by performing additional computation in a `buffer' to reduce breaking depen","authors_text":"Christopher Aicher, Emily B. Fox, Nicholas J. Foti, Yi-An Ma","cross_cats":["cs.LG","stat.CO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-22T05:53:22Z","title":"Stochastic Gradient MCMC for State Space Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.09098","kind":"arxiv","version":2},"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":"sha256:5f67fc971236ed59230bb4e8802100fc8ecbbfc8da85b548db8c98a04ea1bb1e","target":"record","created_at":"2026-05-17T23:41:02Z","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":"4b13ecd05f73095223f9c6efeca8c155849f3439a6a02acc6f5a9dba67e7c9b5","cross_cats_sorted":["cs.LG","stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-22T05:53:22Z","title_canon_sha256":"c6300d6d6e5c70034cffa8686ffe7f6ffdf1e806fdba49307840e1dd4360bee1"},"schema_version":"1.0","source":{"id":"1810.09098","kind":"arxiv","version":2}},"canonical_sha256":"d53c41ef5a16e3bfe9c42d8aee7b2ee6df9ed407214c5d914008d53180e42622","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d53c41ef5a16e3bfe9c42d8aee7b2ee6df9ed407214c5d914008d53180e42622","first_computed_at":"2026-05-17T23:41:02.287546Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:41:02.287546Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vyNVF0Z4Qv6GTs1gYVlh5UeE2acA6Q/dSmZSmdbVS5jOpsEiGCLu3fSlLDO5e/WZKA4yF0J7+k4m+1T5h49VAw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:41:02.288027Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.09098","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5f67fc971236ed59230bb4e8802100fc8ecbbfc8da85b548db8c98a04ea1bb1e","sha256:5dd50837ac85c06b9ae5ac0598d426587a392c4976010b9244ab4878ab61cb0a"],"state_sha256":"f0a7cf04ce6c751482eeebd1d19e3e58caeac960544a48a6017e93b2743a3f40"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h2VOQY1jHpiDSb6/JYYVLNWSEyokLRM6FD9FuOWvQvuqzCYzhsyh4aWJ+3sQvbSVNreJtNNj1flBvy64MK/jBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T19:37:48.560031Z","bundle_sha256":"b03d8e6102086b82f7fddb0000b38b403c182dfdbcd64d5c61af561e6c28cc8a"}}