{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:BCGQZ32H3TJSB43KCNQORXTQW5","short_pith_number":"pith:BCGQZ32H","schema_version":"1.0","canonical_sha256":"088d0cef47dcd320f36a1360e8de70b76c15187c2ddd4b39427ae3902ea618f4","source":{"kind":"arxiv","id":"1412.7160","version":1},"attestation_state":"computed","paper":{"title":"The NIFTY way of Bayesian signal inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.MS","math.IT","physics.data-an"],"primary_cat":"astro-ph.IM","authors_text":"Marco Selig","submitted_at":"2014-12-22T21:00:07Z","abstract_excerpt":"We introduce NIFTY, \"Numerical Information Field Theory\", a software package for the development of Bayesian signal inference algorithms that operate independently from any underlying spatial grid and its resolution. A large number of Bayesian and Maximum Entropy methods for 1D signal reconstruction, 2D imaging, as well as 3D tomography, appear formally similar, but one often finds individualized implementations that are neither flexible nor easily transferable. Signal inference in the framework of NIFTY can be done in an abstract way, such that algorithms, prototyped in 1D, can be applied to "},"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":"1412.7160","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2014-12-22T21:00:07Z","cross_cats_sorted":["cs.IT","cs.MS","math.IT","physics.data-an"],"title_canon_sha256":"e229720f7ba93b75fd9ad2d2b8ccdf8f079a1cf117afb9b0707e742cef3ce2d9","abstract_canon_sha256":"4f19bb1dac476791f6821a61c29bda798c58505664e6dc1921abb4e624a16a77"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:30:40.883647Z","signature_b64":"xkoqK9louOlmHLO9PJRVmTapNMjcb39vEuTNDJa3CT1PFok5EIZimgj9mWKxIXe/TL5XI83sGShe86sF9eZRAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"088d0cef47dcd320f36a1360e8de70b76c15187c2ddd4b39427ae3902ea618f4","last_reissued_at":"2026-05-18T02:30:40.883201Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:30:40.883201Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The NIFTY way of Bayesian signal inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.MS","math.IT","physics.data-an"],"primary_cat":"astro-ph.IM","authors_text":"Marco Selig","submitted_at":"2014-12-22T21:00:07Z","abstract_excerpt":"We introduce NIFTY, \"Numerical Information Field Theory\", a software package for the development of Bayesian signal inference algorithms that operate independently from any underlying spatial grid and its resolution. A large number of Bayesian and Maximum Entropy methods for 1D signal reconstruction, 2D imaging, as well as 3D tomography, appear formally similar, but one often finds individualized implementations that are neither flexible nor easily transferable. Signal inference in the framework of NIFTY can be done in an abstract way, such that algorithms, prototyped in 1D, can be applied to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.7160","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":""},"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":"1412.7160","created_at":"2026-05-18T02:30:40.883257+00:00"},{"alias_kind":"arxiv_version","alias_value":"1412.7160v1","created_at":"2026-05-18T02:30:40.883257+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.7160","created_at":"2026-05-18T02:30:40.883257+00:00"},{"alias_kind":"pith_short_12","alias_value":"BCGQZ32H3TJS","created_at":"2026-05-18T12:28:22.404517+00:00"},{"alias_kind":"pith_short_16","alias_value":"BCGQZ32H3TJSB43K","created_at":"2026-05-18T12:28:22.404517+00:00"},{"alias_kind":"pith_short_8","alias_value":"BCGQZ32H","created_at":"2026-05-18T12:28:22.404517+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/BCGQZ32H3TJSB43KCNQORXTQW5","json":"https://pith.science/pith/BCGQZ32H3TJSB43KCNQORXTQW5.json","graph_json":"https://pith.science/api/pith-number/BCGQZ32H3TJSB43KCNQORXTQW5/graph.json","events_json":"https://pith.science/api/pith-number/BCGQZ32H3TJSB43KCNQORXTQW5/events.json","paper":"https://pith.science/paper/BCGQZ32H"},"agent_actions":{"view_html":"https://pith.science/pith/BCGQZ32H3TJSB43KCNQORXTQW5","download_json":"https://pith.science/pith/BCGQZ32H3TJSB43KCNQORXTQW5.json","view_paper":"https://pith.science/paper/BCGQZ32H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1412.7160&json=true","fetch_graph":"https://pith.science/api/pith-number/BCGQZ32H3TJSB43KCNQORXTQW5/graph.json","fetch_events":"https://pith.science/api/pith-number/BCGQZ32H3TJSB43KCNQORXTQW5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BCGQZ32H3TJSB43KCNQORXTQW5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BCGQZ32H3TJSB43KCNQORXTQW5/action/storage_attestation","attest_author":"https://pith.science/pith/BCGQZ32H3TJSB43KCNQORXTQW5/action/author_attestation","sign_citation":"https://pith.science/pith/BCGQZ32H3TJSB43KCNQORXTQW5/action/citation_signature","submit_replication":"https://pith.science/pith/BCGQZ32H3TJSB43KCNQORXTQW5/action/replication_record"}},"created_at":"2026-05-18T02:30:40.883257+00:00","updated_at":"2026-05-18T02:30:40.883257+00:00"}