{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:MLZTYGEDH6DFWMU2CVYOWKIT4L","short_pith_number":"pith:MLZTYGED","schema_version":"1.0","canonical_sha256":"62f33c18833f865b329a1570eb2913e2d2b0fa96e01f9d45a5cb6f30fa9c5156","source":{"kind":"arxiv","id":"1911.05865","version":5},"attestation_state":"computed","paper":{"title":"Beyond Mat\\'ern: On A Class of Interpretable Confluent Hypergeometric Covariance Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Anindya Bhadra, Pulong Ma","submitted_at":"2019-11-14T00:01:07Z","abstract_excerpt":"The Mat\\'ern covariance function is a popular choice for prediction in spatial statistics and uncertainty quantification literature. A key benefit of the Mat\\'ern class is that it is possible to get precise control over the degree of mean-square differentiability of the random process. However, the Mat\\'ern class possesses exponentially decaying tails, and thus may not be suitable for modeling polynomially decaying dependence. This problem can be remedied using polynomial covariances; however one loses control over the degree of mean-square differentiability of corresponding processes, in that"},"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":"1911.05865","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-11-14T00:01:07Z","cross_cats_sorted":["stat.ME","stat.ML","stat.TH"],"title_canon_sha256":"89f3b76e2cc2e235f95f5d8e27059ea1fbedac5b152d6d3d0b1250d03ddb7ab4","abstract_canon_sha256":"8bbaa21ad0937db18a70f2b9ab9d9f68cfbdab79fdb65a0e7d6258a8aa0bd5d0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:28:09.095407Z","signature_b64":"0ryrLHnNGSJLGGMeDwS6CaGB7FXk2rlTZSIg1FNrEzXtsrYpvruYD8R1UfbNZk6Rl39r5Ey0ZGj5z7VmdUSVBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"62f33c18833f865b329a1570eb2913e2d2b0fa96e01f9d45a5cb6f30fa9c5156","last_reissued_at":"2026-07-05T03:28:09.095000Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:28:09.095000Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond Mat\\'ern: On A Class of Interpretable Confluent Hypergeometric Covariance Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Anindya Bhadra, Pulong Ma","submitted_at":"2019-11-14T00:01:07Z","abstract_excerpt":"The Mat\\'ern covariance function is a popular choice for prediction in spatial statistics and uncertainty quantification literature. A key benefit of the Mat\\'ern class is that it is possible to get precise control over the degree of mean-square differentiability of the random process. However, the Mat\\'ern class possesses exponentially decaying tails, and thus may not be suitable for modeling polynomially decaying dependence. This problem can be remedied using polynomial covariances; however one loses control over the degree of mean-square differentiability of corresponding processes, in that"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.05865","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1911.05865/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":"1911.05865","created_at":"2026-07-05T03:28:09.095057+00:00"},{"alias_kind":"arxiv_version","alias_value":"1911.05865v5","created_at":"2026-07-05T03:28:09.095057+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.05865","created_at":"2026-07-05T03:28:09.095057+00:00"},{"alias_kind":"pith_short_12","alias_value":"MLZTYGEDH6DF","created_at":"2026-07-05T03:28:09.095057+00:00"},{"alias_kind":"pith_short_16","alias_value":"MLZTYGEDH6DFWMU2","created_at":"2026-07-05T03:28:09.095057+00:00"},{"alias_kind":"pith_short_8","alias_value":"MLZTYGED","created_at":"2026-07-05T03:28:09.095057+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/MLZTYGEDH6DFWMU2CVYOWKIT4L","json":"https://pith.science/pith/MLZTYGEDH6DFWMU2CVYOWKIT4L.json","graph_json":"https://pith.science/api/pith-number/MLZTYGEDH6DFWMU2CVYOWKIT4L/graph.json","events_json":"https://pith.science/api/pith-number/MLZTYGEDH6DFWMU2CVYOWKIT4L/events.json","paper":"https://pith.science/paper/MLZTYGED"},"agent_actions":{"view_html":"https://pith.science/pith/MLZTYGEDH6DFWMU2CVYOWKIT4L","download_json":"https://pith.science/pith/MLZTYGEDH6DFWMU2CVYOWKIT4L.json","view_paper":"https://pith.science/paper/MLZTYGED","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1911.05865&json=true","fetch_graph":"https://pith.science/api/pith-number/MLZTYGEDH6DFWMU2CVYOWKIT4L/graph.json","fetch_events":"https://pith.science/api/pith-number/MLZTYGEDH6DFWMU2CVYOWKIT4L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MLZTYGEDH6DFWMU2CVYOWKIT4L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MLZTYGEDH6DFWMU2CVYOWKIT4L/action/storage_attestation","attest_author":"https://pith.science/pith/MLZTYGEDH6DFWMU2CVYOWKIT4L/action/author_attestation","sign_citation":"https://pith.science/pith/MLZTYGEDH6DFWMU2CVYOWKIT4L/action/citation_signature","submit_replication":"https://pith.science/pith/MLZTYGEDH6DFWMU2CVYOWKIT4L/action/replication_record"}},"created_at":"2026-07-05T03:28:09.095057+00:00","updated_at":"2026-07-05T03:28:09.095057+00:00"}