{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:YLVQ45MIBM6MK6SN5CC3KXYQBH","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":"defebbc0238ed3ba9c219bfe782382b91047c68029f610b768da79eddee8e346","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2022-03-09T14:15:02Z","title_canon_sha256":"46bf275758ce66497a3607e6a850f1dec6274a721708a0b7f87e59e6f5c70953"},"schema_version":"1.0","source":{"id":"2203.04733","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.04733","created_at":"2026-07-05T05:04:12Z"},{"alias_kind":"arxiv_version","alias_value":"2203.04733v3","created_at":"2026-07-05T05:04:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.04733","created_at":"2026-07-05T05:04:12Z"},{"alias_kind":"pith_short_12","alias_value":"YLVQ45MIBM6M","created_at":"2026-07-05T05:04:12Z"},{"alias_kind":"pith_short_16","alias_value":"YLVQ45MIBM6MK6SN","created_at":"2026-07-05T05:04:12Z"},{"alias_kind":"pith_short_8","alias_value":"YLVQ45MI","created_at":"2026-07-05T05:04:12Z"}],"graph_snapshots":[{"event_id":"sha256:1fad4be8e6c0f4c9dbbb1b2317bf51f07bc753619e787dbac1563a88fae2e265","target":"graph","created_at":"2026-07-05T05:04:12Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2203.04733/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modeling with multidimensional arrays, or tensors, often presents a problem due to high dimensionality. In addition, these structures typically exhibit inherent sparsity, requiring the use of regularization methods to properly characterize an association between a tensor covariate and a scalar response. We propose a Bayesian method to efficiently model a scalar response with a tensor covariate using the Tucker tensor decomposition in order to retain the spatial relationship within a tensor coefficient, while reducing the number of parameters varying within the model and applying regularization","authors_text":"Daniel Spencer, Rajarshi Guhaniyogi, Raquel Prado, Russell Shinohara","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2022-03-09T14:15:02Z","title":"Bayesian tensor regression using the Tucker decomposition for sparse spatial modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.04733","kind":"arxiv","version":3},"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:db89d3096ea85c7370899d85d388ec3c0243dc35ff0c16663efdd9708a167654","target":"record","created_at":"2026-07-05T05:04:12Z","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":"defebbc0238ed3ba9c219bfe782382b91047c68029f610b768da79eddee8e346","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2022-03-09T14:15:02Z","title_canon_sha256":"46bf275758ce66497a3607e6a850f1dec6274a721708a0b7f87e59e6f5c70953"},"schema_version":"1.0","source":{"id":"2203.04733","kind":"arxiv","version":3}},"canonical_sha256":"c2eb0e75880b3cc57a4de885b55f1009ebbeb2f19ac361959dd2750d5f0115f7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c2eb0e75880b3cc57a4de885b55f1009ebbeb2f19ac361959dd2750d5f0115f7","first_computed_at":"2026-07-05T05:04:12.323102Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:04:12.323102Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6yOm/WZ2pAITrtt5a7f8YRJXtaWtKG8vvBlC6Z9C6IDPwOXzYG1ABOaOx2KItt9GJSxrPu8rHR/lLCljEl7KDw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:04:12.323636Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.04733","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:db89d3096ea85c7370899d85d388ec3c0243dc35ff0c16663efdd9708a167654","sha256:1fad4be8e6c0f4c9dbbb1b2317bf51f07bc753619e787dbac1563a88fae2e265"],"state_sha256":"6a18739a20e1dc63d17a74f44f4a60fabd05330a5016da440adf0f30d819a34b"}