{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:6ZJIYWAKO325QAXJGBR5PW2ASJ","short_pith_number":"pith:6ZJIYWAK","canonical_record":{"source":{"id":"2602.08544","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-02-09T11:45:01Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"3072f9202ee592e5902123eba87ba721ce9bcb537eb54cef76c5c59981b7ac9d","abstract_canon_sha256":"36006fcccc15dd85e92f2a353f01d53affa41d8585f35f0ee7970ef702632fa3"},"schema_version":"1.0"},"canonical_sha256":"f6528c580a76f5d802e93063d7db4092790a381c892d27acd2420779c60ad68b","source":{"kind":"arxiv","id":"2602.08544","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.08544","created_at":"2026-07-22T00:22:16Z"},{"alias_kind":"arxiv_version","alias_value":"2602.08544v2","created_at":"2026-07-22T00:22:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.08544","created_at":"2026-07-22T00:22:16Z"},{"alias_kind":"pith_short_12","alias_value":"6ZJIYWAKO325","created_at":"2026-07-22T00:22:16Z"},{"alias_kind":"pith_short_16","alias_value":"6ZJIYWAKO325QAXJ","created_at":"2026-07-22T00:22:16Z"},{"alias_kind":"pith_short_8","alias_value":"6ZJIYWAK","created_at":"2026-07-22T00:22:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:6ZJIYWAKO325QAXJGBR5PW2ASJ","target":"record","payload":{"canonical_record":{"source":{"id":"2602.08544","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-02-09T11:45:01Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"3072f9202ee592e5902123eba87ba721ce9bcb537eb54cef76c5c59981b7ac9d","abstract_canon_sha256":"36006fcccc15dd85e92f2a353f01d53affa41d8585f35f0ee7970ef702632fa3"},"schema_version":"1.0"},"canonical_sha256":"f6528c580a76f5d802e93063d7db4092790a381c892d27acd2420779c60ad68b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-22T00:22:16.391830Z","signature_b64":"/DyJ5KIpnv60m6RGjqcFpD8F5U5hmwuAoT23XaiSMQm87Iqs1vFHfkzDJ3Glgo6alKtwllZ2bwoLeWRjNETeAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f6528c580a76f5d802e93063d7db4092790a381c892d27acd2420779c60ad68b","last_reissued_at":"2026-07-22T00:22:16.390818Z","signature_status":"signed_v1","first_computed_at":"2026-07-22T00:22:16.390818Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2602.08544","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":"2026-07-22T00:22:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1/AxOWk15oi+nOzc9seB1rpSzpurZZTjqGU6aLj58/dFOyRgH3+h65KLVDw6pWb0hA3Vq+XFcwb9UtB3pI/yAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T13:24:47.826290Z"},"content_sha256":"644b5925284ea838f6f534e704d5361cfe9b95acc2b2bcd6a34a477d3e6e702e","schema_version":"1.0","event_id":"sha256:644b5925284ea838f6f534e704d5361cfe9b95acc2b2bcd6a34a477d3e6e702e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:6ZJIYWAKO325QAXJGBR5PW2ASJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dynamic Bayesian Predictive Stacking via Markovian Spatiotemporal Propagation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Luca Presicce, Sudipto Banerjee","submitted_at":"2026-02-09T11:45:01Z","abstract_excerpt":"This manuscript develops computationally efficient online learning for multivariate spatiotemporal models. The proposed framework relies on matrix-variate Gaussian distributions, dynamic linear models, and Bayesian predictive stacking to efficiently share information across temporal data shards. The model facilitates effective information propagation over time while seamlessly integrating spatial components within a dynamic framework, building a Markovian dependence structure between datasets at successive time instants. This structure supports flexible, high-dimensional modeling of complex de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.08544","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2602.08544/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-22T00:22:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tLs9OwT88KYZjMZhh6sG4DkdgW6m2A8TavVMFAdbDJ/bzWn2jXERWbmRYP0xciOr9URZtNIlAe2k1Lmo2yBEAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T13:24:47.827519Z"},"content_sha256":"188366126b6fb914dbe959f8e12f5f25d6d23a31c582239efdf080017dab43a3","schema_version":"1.0","event_id":"sha256:188366126b6fb914dbe959f8e12f5f25d6d23a31c582239efdf080017dab43a3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6ZJIYWAKO325QAXJGBR5PW2ASJ/bundle.json","state_url":"https://pith.science/pith/6ZJIYWAKO325QAXJGBR5PW2ASJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6ZJIYWAKO325QAXJGBR5PW2ASJ/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":"2026-08-15T13:24:47Z","links":{"resolver":"https://pith.science/pith/6ZJIYWAKO325QAXJGBR5PW2ASJ","bundle":"https://pith.science/pith/6ZJIYWAKO325QAXJGBR5PW2ASJ/bundle.json","state":"https://pith.science/pith/6ZJIYWAKO325QAXJGBR5PW2ASJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6ZJIYWAKO325QAXJGBR5PW2ASJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:6ZJIYWAKO325QAXJGBR5PW2ASJ","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":"36006fcccc15dd85e92f2a353f01d53affa41d8585f35f0ee7970ef702632fa3","cross_cats_sorted":["stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-02-09T11:45:01Z","title_canon_sha256":"3072f9202ee592e5902123eba87ba721ce9bcb537eb54cef76c5c59981b7ac9d"},"schema_version":"1.0","source":{"id":"2602.08544","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.08544","created_at":"2026-07-22T00:22:16Z"},{"alias_kind":"arxiv_version","alias_value":"2602.08544v2","created_at":"2026-07-22T00:22:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.08544","created_at":"2026-07-22T00:22:16Z"},{"alias_kind":"pith_short_12","alias_value":"6ZJIYWAKO325","created_at":"2026-07-22T00:22:16Z"},{"alias_kind":"pith_short_16","alias_value":"6ZJIYWAKO325QAXJ","created_at":"2026-07-22T00:22:16Z"},{"alias_kind":"pith_short_8","alias_value":"6ZJIYWAK","created_at":"2026-07-22T00:22:16Z"}],"graph_snapshots":[{"event_id":"sha256:188366126b6fb914dbe959f8e12f5f25d6d23a31c582239efdf080017dab43a3","target":"graph","created_at":"2026-07-22T00:22:16Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2602.08544/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This manuscript develops computationally efficient online learning for multivariate spatiotemporal models. The proposed framework relies on matrix-variate Gaussian distributions, dynamic linear models, and Bayesian predictive stacking to efficiently share information across temporal data shards. The model facilitates effective information propagation over time while seamlessly integrating spatial components within a dynamic framework, building a Markovian dependence structure between datasets at successive time instants. This structure supports flexible, high-dimensional modeling of complex de","authors_text":"Luca Presicce, Sudipto Banerjee","cross_cats":["stat.CO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-02-09T11:45:01Z","title":"Dynamic Bayesian Predictive Stacking via Markovian Spatiotemporal Propagation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.08544","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":"sha256:644b5925284ea838f6f534e704d5361cfe9b95acc2b2bcd6a34a477d3e6e702e","target":"record","created_at":"2026-07-22T00:22:16Z","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":"36006fcccc15dd85e92f2a353f01d53affa41d8585f35f0ee7970ef702632fa3","cross_cats_sorted":["stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-02-09T11:45:01Z","title_canon_sha256":"3072f9202ee592e5902123eba87ba721ce9bcb537eb54cef76c5c59981b7ac9d"},"schema_version":"1.0","source":{"id":"2602.08544","kind":"arxiv","version":2}},"canonical_sha256":"f6528c580a76f5d802e93063d7db4092790a381c892d27acd2420779c60ad68b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f6528c580a76f5d802e93063d7db4092790a381c892d27acd2420779c60ad68b","first_computed_at":"2026-07-22T00:22:16.390818Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-22T00:22:16.390818Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/DyJ5KIpnv60m6RGjqcFpD8F5U5hmwuAoT23XaiSMQm87Iqs1vFHfkzDJ3Glgo6alKtwllZ2bwoLeWRjNETeAw==","signature_status":"signed_v1","signed_at":"2026-07-22T00:22:16.391830Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.08544","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:644b5925284ea838f6f534e704d5361cfe9b95acc2b2bcd6a34a477d3e6e702e","sha256:188366126b6fb914dbe959f8e12f5f25d6d23a31c582239efdf080017dab43a3"],"state_sha256":"b3c3270410248bec8a76ad84b6d192e2f3f4a6b43bbb7cae9c9eedf2316d5084"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"csoen7UJN1BPdCEohyqaYIgqT3g4+Oq25tszag3AITFoLOVtbYEUHUw6k2raayq+xitcxjNmWfEDgLmYWAMWCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T13:24:47.837612Z","bundle_sha256":"3ccd59ed231ecf808c138e78387e3424803cab80523de3074c511dc1aa15f6d2"}}