{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:E7ZYFNSFGFB4ZZSFFYQJMAERAB","short_pith_number":"pith:E7ZYFNSF","schema_version":"1.0","canonical_sha256":"27f382b6453143cce6452e20960091007c2ab9345a7f6a8579aa5ef63c3a2636","source":{"kind":"arxiv","id":"2310.02600","version":3},"attestation_state":"computed","paper":{"title":"Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Andrew Zammit-Mangion, Jordan Richards, Matthew Sainsbury-Dale, Rapha\\\"el Huser","submitted_at":"2023-10-04T06:13:22Z","abstract_excerpt":"Neural Bayes estimators are neural networks that approximate Bayes estimators in a fast and likelihood-free manner. Although they are appealing to use with spatial models, where estimation is often a computational bottleneck, neural Bayes estimators in spatial applications have, to date, been restricted to data collected over a regular grid. These estimators are also currently dependent on a prescribed set of spatial locations, which means that the neural network needs to be re-trained for new data sets; this renders them impractical in many applications and impedes their widespread adoption. "},"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":"2310.02600","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2023-10-04T06:13:22Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"63a9681f40b20ce3de1eae1d08937701fe5ea24ba8318c482411761b0ed4c12a","abstract_canon_sha256":"510b78cfc87bb9b339d0b29e1507295565a3394c27e523935a45a9e5efa5d414"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:59:42.492790Z","signature_b64":"Yt+o3PBfs/oB/k2JJS4o42otmJfygsHGipVhdku0vwRwodfxd6rPQY/G3ncDDD/4YrLkWqhNBrFfgtBSObv9AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"27f382b6453143cce6452e20960091007c2ab9345a7f6a8579aa5ef63c3a2636","last_reissued_at":"2026-07-05T09:59:42.492255Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:59:42.492255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Andrew Zammit-Mangion, Jordan Richards, Matthew Sainsbury-Dale, Rapha\\\"el Huser","submitted_at":"2023-10-04T06:13:22Z","abstract_excerpt":"Neural Bayes estimators are neural networks that approximate Bayes estimators in a fast and likelihood-free manner. Although they are appealing to use with spatial models, where estimation is often a computational bottleneck, neural Bayes estimators in spatial applications have, to date, been restricted to data collected over a regular grid. These estimators are also currently dependent on a prescribed set of spatial locations, which means that the neural network needs to be re-trained for new data sets; this renders them impractical in many applications and impedes their widespread adoption. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.02600","kind":"arxiv","version":3},"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/2310.02600/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":"2310.02600","created_at":"2026-07-05T09:59:42.492326+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.02600v3","created_at":"2026-07-05T09:59:42.492326+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.02600","created_at":"2026-07-05T09:59:42.492326+00:00"},{"alias_kind":"pith_short_12","alias_value":"E7ZYFNSFGFB4","created_at":"2026-07-05T09:59:42.492326+00:00"},{"alias_kind":"pith_short_16","alias_value":"E7ZYFNSFGFB4ZZSF","created_at":"2026-07-05T09:59:42.492326+00:00"},{"alias_kind":"pith_short_8","alias_value":"E7ZYFNSF","created_at":"2026-07-05T09:59:42.492326+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.17400","citing_title":"A Generalized Unified Skew-Normal Process with Neural Bayes Inference","ref_index":29,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E7ZYFNSFGFB4ZZSFFYQJMAERAB","json":"https://pith.science/pith/E7ZYFNSFGFB4ZZSFFYQJMAERAB.json","graph_json":"https://pith.science/api/pith-number/E7ZYFNSFGFB4ZZSFFYQJMAERAB/graph.json","events_json":"https://pith.science/api/pith-number/E7ZYFNSFGFB4ZZSFFYQJMAERAB/events.json","paper":"https://pith.science/paper/E7ZYFNSF"},"agent_actions":{"view_html":"https://pith.science/pith/E7ZYFNSFGFB4ZZSFFYQJMAERAB","download_json":"https://pith.science/pith/E7ZYFNSFGFB4ZZSFFYQJMAERAB.json","view_paper":"https://pith.science/paper/E7ZYFNSF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.02600&json=true","fetch_graph":"https://pith.science/api/pith-number/E7ZYFNSFGFB4ZZSFFYQJMAERAB/graph.json","fetch_events":"https://pith.science/api/pith-number/E7ZYFNSFGFB4ZZSFFYQJMAERAB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E7ZYFNSFGFB4ZZSFFYQJMAERAB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E7ZYFNSFGFB4ZZSFFYQJMAERAB/action/storage_attestation","attest_author":"https://pith.science/pith/E7ZYFNSFGFB4ZZSFFYQJMAERAB/action/author_attestation","sign_citation":"https://pith.science/pith/E7ZYFNSFGFB4ZZSFFYQJMAERAB/action/citation_signature","submit_replication":"https://pith.science/pith/E7ZYFNSFGFB4ZZSFFYQJMAERAB/action/replication_record"}},"created_at":"2026-07-05T09:59:42.492326+00:00","updated_at":"2026-07-05T09:59:42.492326+00:00"}