{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:NCYFW7QO2WIHPHBAU3BQJCGJR5","short_pith_number":"pith:NCYFW7QO","schema_version":"1.0","canonical_sha256":"68b05b7e0ed590779c20a6c30488c98f5c55f30a89f8ccfdb400041ed36a9ba0","source":{"kind":"arxiv","id":"2005.05464","version":1},"attestation_state":"computed","paper":{"title":"Non-Separable Spatio-temporal Models via Transformed Gaussian Markov Random Fields","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Douglas R. M. Azevedo, Marcos O. Prates, Michael R. Willig","submitted_at":"2020-05-11T22:07:35Z","abstract_excerpt":"Models that capture the spatial and temporal dynamics are applicable in many science fields. Non-separable spatio-temporal models were introduced in the literature to capture these features. However, these models are generally complicated in construction and interpretation. We introduce a class of non-separable Transformed Gaussian Markov Random Fields (TGMRF) in which the dependence structure is flexible and facilitates simple interpretations concerning spatial, temporal and spatio-temporal parameters. Moreover, TGMRF models have the advantage of allowing specialists to define any desired mar"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2005.05464","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2020-05-11T22:07:35Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"ae075accfde61dd037d2c228d19b1c9d24fe95eef6aa77725944efc02f073d0c","abstract_canon_sha256":"10c0c251e4e8e681ac3cab794003cb031a1559cce971a6e97fb9326862b3d69b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:02:20.804359Z","signature_b64":"ESqIuBh9tmbFRWQdq7IagNrXS/BUyCH9GzmbSYJ6EhNVdVkmcejthNGwT9hh7M54FHoXRgFybPpIyqfdc2L+DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"68b05b7e0ed590779c20a6c30488c98f5c55f30a89f8ccfdb400041ed36a9ba0","last_reissued_at":"2026-07-05T01:02:20.803825Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:02:20.803825Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Non-Separable Spatio-temporal Models via Transformed Gaussian Markov Random Fields","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Douglas R. M. Azevedo, Marcos O. Prates, Michael R. Willig","submitted_at":"2020-05-11T22:07:35Z","abstract_excerpt":"Models that capture the spatial and temporal dynamics are applicable in many science fields. Non-separable spatio-temporal models were introduced in the literature to capture these features. However, these models are generally complicated in construction and interpretation. We introduce a class of non-separable Transformed Gaussian Markov Random Fields (TGMRF) in which the dependence structure is flexible and facilitates simple interpretations concerning spatial, temporal and spatio-temporal parameters. Moreover, TGMRF models have the advantage of allowing specialists to define any desired mar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.05464","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2005.05464/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2005.05464","created_at":"2026-07-05T01:02:20.803898+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.05464v1","created_at":"2026-07-05T01:02:20.803898+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.05464","created_at":"2026-07-05T01:02:20.803898+00:00"},{"alias_kind":"pith_short_12","alias_value":"NCYFW7QO2WIH","created_at":"2026-07-05T01:02:20.803898+00:00"},{"alias_kind":"pith_short_16","alias_value":"NCYFW7QO2WIHPHBA","created_at":"2026-07-05T01:02:20.803898+00:00"},{"alias_kind":"pith_short_8","alias_value":"NCYFW7QO","created_at":"2026-07-05T01:02:20.803898+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NCYFW7QO2WIHPHBAU3BQJCGJR5","json":"https://pith.science/pith/NCYFW7QO2WIHPHBAU3BQJCGJR5.json","graph_json":"https://pith.science/api/pith-number/NCYFW7QO2WIHPHBAU3BQJCGJR5/graph.json","events_json":"https://pith.science/api/pith-number/NCYFW7QO2WIHPHBAU3BQJCGJR5/events.json","paper":"https://pith.science/paper/NCYFW7QO"},"agent_actions":{"view_html":"https://pith.science/pith/NCYFW7QO2WIHPHBAU3BQJCGJR5","download_json":"https://pith.science/pith/NCYFW7QO2WIHPHBAU3BQJCGJR5.json","view_paper":"https://pith.science/paper/NCYFW7QO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.05464&json=true","fetch_graph":"https://pith.science/api/pith-number/NCYFW7QO2WIHPHBAU3BQJCGJR5/graph.json","fetch_events":"https://pith.science/api/pith-number/NCYFW7QO2WIHPHBAU3BQJCGJR5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NCYFW7QO2WIHPHBAU3BQJCGJR5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NCYFW7QO2WIHPHBAU3BQJCGJR5/action/storage_attestation","attest_author":"https://pith.science/pith/NCYFW7QO2WIHPHBAU3BQJCGJR5/action/author_attestation","sign_citation":"https://pith.science/pith/NCYFW7QO2WIHPHBAU3BQJCGJR5/action/citation_signature","submit_replication":"https://pith.science/pith/NCYFW7QO2WIHPHBAU3BQJCGJR5/action/replication_record"}},"created_at":"2026-07-05T01:02:20.803898+00:00","updated_at":"2026-07-05T01:02:20.803898+00:00"}