{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:XHV43ZLETPSDEF4SYYB322KH4U","short_pith_number":"pith:XHV43ZLE","canonical_record":{"source":{"id":"1506.02691","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-06-08T20:42:52Z","cross_cats_sorted":[],"title_canon_sha256":"c78dbb80e9364774fb1e58b1574724505d45ed477aca8685a14602a984a8d8ab","abstract_canon_sha256":"940a6306a15abd94b939aa57c2df13a62da136ff40b54de4700ddca29fa1fd49"},"schema_version":"1.0"},"canonical_sha256":"b9ebcde5649be4321792c603bd6947e51db881b97734914ca9965683b3b39184","source":{"kind":"arxiv","id":"1506.02691","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.02691","created_at":"2026-05-18T00:38:12Z"},{"alias_kind":"arxiv_version","alias_value":"1506.02691v5","created_at":"2026-05-18T00:38:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.02691","created_at":"2026-05-18T00:38:12Z"},{"alias_kind":"pith_short_12","alias_value":"XHV43ZLETPSD","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_16","alias_value":"XHV43ZLETPSDEF4S","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_8","alias_value":"XHV43ZLE","created_at":"2026-05-18T12:29:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:XHV43ZLETPSDEF4SYYB322KH4U","target":"record","payload":{"canonical_record":{"source":{"id":"1506.02691","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-06-08T20:42:52Z","cross_cats_sorted":[],"title_canon_sha256":"c78dbb80e9364774fb1e58b1574724505d45ed477aca8685a14602a984a8d8ab","abstract_canon_sha256":"940a6306a15abd94b939aa57c2df13a62da136ff40b54de4700ddca29fa1fd49"},"schema_version":"1.0"},"canonical_sha256":"b9ebcde5649be4321792c603bd6947e51db881b97734914ca9965683b3b39184","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:12.915846Z","signature_b64":"jAzO/tpg843Q5eT3zgjk9PZnV3xwphp8JlmK5aSvQzEjwAw4C757MaTd7swo1TDkE5I64WSuQUsLD6G0GBYoDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b9ebcde5649be4321792c603bd6947e51db881b97734914ca9965683b3b39184","last_reissued_at":"2026-05-18T00:38:12.915193Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:12.915193Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1506.02691","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:38:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Sj3Ekop7KcwuYs0KvjNG47RVhnQ/Xy7AaBU76NMznYT94L0fEXIDQ4QekBgh0sKT8k9aaeGOP82DblPT/Wq2DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:48:12.597633Z"},"content_sha256":"fc95b8938387b66650f23a12367097e06140dd2de0fff8c8c3d3aeaa428f5cdd","schema_version":"1.0","event_id":"sha256:fc95b8938387b66650f23a12367097e06140dd2de0fff8c8c3d3aeaa428f5cdd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:XHV43ZLETPSDEF4SYYB322KH4U","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sequential Empirical Bayes method for filtering dynamic spatiotemporal processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Evangelos Evangelou, Vasileios Maroulas","submitted_at":"2015-06-08T20:42:52Z","abstract_excerpt":"We consider online prediction of a latent dynamic spatiotemporal process and estimation of the associated model parameters based on noisy data. The problem is motivated by the analysis of spatial data arriving in real-time and the current parameter estimates and predictions are updated using the new data at a fixed computational cost. Estimation and prediction is performed within an empirical Bayes framework with the aid of Markov chain Monte Carlo samples. Samples for the latent spatial field are generated using a sampling importance resampling algorithm with a skewed-normal proposal and for "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.02691","kind":"arxiv","version":5},"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":"2026-05-18T00:38:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"etC0JcF+nRndMzSG/mdBE7b8YqgwX2THCxCSw9ubZrZWGUPGc9PSIj9uu4xvg3L0Jd9C0hOs/iXb+3LaD2glCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:48:12.598303Z"},"content_sha256":"f4bfb9bae9f99e32c4ceedfdfa1cbe192363f94f5f7c7a7fbcfceab2d8f611de","schema_version":"1.0","event_id":"sha256:f4bfb9bae9f99e32c4ceedfdfa1cbe192363f94f5f7c7a7fbcfceab2d8f611de"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XHV43ZLETPSDEF4SYYB322KH4U/bundle.json","state_url":"https://pith.science/pith/XHV43ZLETPSDEF4SYYB322KH4U/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XHV43ZLETPSDEF4SYYB322KH4U/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":"2026-05-26T11:48:12Z","links":{"resolver":"https://pith.science/pith/XHV43ZLETPSDEF4SYYB322KH4U","bundle":"https://pith.science/pith/XHV43ZLETPSDEF4SYYB322KH4U/bundle.json","state":"https://pith.science/pith/XHV43ZLETPSDEF4SYYB322KH4U/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XHV43ZLETPSDEF4SYYB322KH4U/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:XHV43ZLETPSDEF4SYYB322KH4U","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":"940a6306a15abd94b939aa57c2df13a62da136ff40b54de4700ddca29fa1fd49","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-06-08T20:42:52Z","title_canon_sha256":"c78dbb80e9364774fb1e58b1574724505d45ed477aca8685a14602a984a8d8ab"},"schema_version":"1.0","source":{"id":"1506.02691","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.02691","created_at":"2026-05-18T00:38:12Z"},{"alias_kind":"arxiv_version","alias_value":"1506.02691v5","created_at":"2026-05-18T00:38:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.02691","created_at":"2026-05-18T00:38:12Z"},{"alias_kind":"pith_short_12","alias_value":"XHV43ZLETPSD","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_16","alias_value":"XHV43ZLETPSDEF4S","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_8","alias_value":"XHV43ZLE","created_at":"2026-05-18T12:29:50Z"}],"graph_snapshots":[{"event_id":"sha256:f4bfb9bae9f99e32c4ceedfdfa1cbe192363f94f5f7c7a7fbcfceab2d8f611de","target":"graph","created_at":"2026-05-18T00:38:12Z","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":"We consider online prediction of a latent dynamic spatiotemporal process and estimation of the associated model parameters based on noisy data. The problem is motivated by the analysis of spatial data arriving in real-time and the current parameter estimates and predictions are updated using the new data at a fixed computational cost. Estimation and prediction is performed within an empirical Bayes framework with the aid of Markov chain Monte Carlo samples. Samples for the latent spatial field are generated using a sampling importance resampling algorithm with a skewed-normal proposal and for ","authors_text":"Evangelos Evangelou, Vasileios Maroulas","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-06-08T20:42:52Z","title":"Sequential Empirical Bayes method for filtering dynamic spatiotemporal processes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.02691","kind":"arxiv","version":5},"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:fc95b8938387b66650f23a12367097e06140dd2de0fff8c8c3d3aeaa428f5cdd","target":"record","created_at":"2026-05-18T00:38:12Z","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":"940a6306a15abd94b939aa57c2df13a62da136ff40b54de4700ddca29fa1fd49","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-06-08T20:42:52Z","title_canon_sha256":"c78dbb80e9364774fb1e58b1574724505d45ed477aca8685a14602a984a8d8ab"},"schema_version":"1.0","source":{"id":"1506.02691","kind":"arxiv","version":5}},"canonical_sha256":"b9ebcde5649be4321792c603bd6947e51db881b97734914ca9965683b3b39184","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b9ebcde5649be4321792c603bd6947e51db881b97734914ca9965683b3b39184","first_computed_at":"2026-05-18T00:38:12.915193Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:38:12.915193Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jAzO/tpg843Q5eT3zgjk9PZnV3xwphp8JlmK5aSvQzEjwAw4C757MaTd7swo1TDkE5I64WSuQUsLD6G0GBYoDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:38:12.915846Z","signed_message":"canonical_sha256_bytes"},"source_id":"1506.02691","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fc95b8938387b66650f23a12367097e06140dd2de0fff8c8c3d3aeaa428f5cdd","sha256:f4bfb9bae9f99e32c4ceedfdfa1cbe192363f94f5f7c7a7fbcfceab2d8f611de"],"state_sha256":"475ce84cbfd994d260e525dd4459ba75dfdf923e27d275e9b201d65c5b27eb1d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OpbWG0lLDRJk3wBKy60EiYoS8Viy1H8Y2pvUQydDvejmQm6UevkNvIP6IQFyFpi86yEnEd98jVfI4KeSI4G9Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T11:48:12.602254Z","bundle_sha256":"ba4e2d279ab7e2adba32df4e2e87d1997fe707101dcb3ee403e728f431c615b5"}}