{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:GIS6BFJXMN7ZA7YFG4WO6S5MVJ","short_pith_number":"pith:GIS6BFJX","schema_version":"1.0","canonical_sha256":"3225e09537637f907f05372cef4bacaa55b0ab2d4b20f3fcb78d5afd30c43405","source":{"kind":"arxiv","id":"2302.04703","version":1},"attestation_state":"computed","paper":{"title":"Practical Guidance for Bayesian Inference in Astronomy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"astro-ph.IM","authors_text":"Aaron Springford, Daniela Huppenkothen, Daniel Foreman-Mackey, David E. Jones, Gwendolyn M. Eadie, Hyungsuk Tak, Jessi Cisewski-Kehe, Joshua S. Speagle","submitted_at":"2023-02-09T15:40:30Z","abstract_excerpt":"In the last two decades, Bayesian inference has become commonplace in astronomy. At the same time, the choice of algorithms, terminology, notation, and interpretation of Bayesian inference varies from one sub-field of astronomy to the next, which can lead to confusion to both those learning and those familiar with Bayesian statistics. Moreover, the choice varies between the astronomy and statistics literature, too. In this paper, our goal is two-fold: (1) provide a reference that consolidates and clarifies terminology and notation across disciplines, and (2) outline practical guidance for Baye"},"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":"2302.04703","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2023-02-09T15:40:30Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"a66e159b4f1954a2bad3e55738c8915dc53fa12cc4a9ec503f760bbba50c788e","abstract_canon_sha256":"79dfdef0a4a3aab05739158cb6597087aeb31972050812d3f9fb448e0389c8f5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:40:17.626277Z","signature_b64":"bcTkLS4pw3HWWCocXkpehXTGfr/v0PsoiU76+yGrRLujTK+eUrf+bTtkcQ0t08jlbBKJ/ED5IXcc9JPrgS12BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3225e09537637f907f05372cef4bacaa55b0ab2d4b20f3fcb78d5afd30c43405","last_reissued_at":"2026-07-05T05:40:17.625875Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:40:17.625875Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Practical Guidance for Bayesian Inference in Astronomy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"astro-ph.IM","authors_text":"Aaron Springford, Daniela Huppenkothen, Daniel Foreman-Mackey, David E. Jones, Gwendolyn M. Eadie, Hyungsuk Tak, Jessi Cisewski-Kehe, Joshua S. Speagle","submitted_at":"2023-02-09T15:40:30Z","abstract_excerpt":"In the last two decades, Bayesian inference has become commonplace in astronomy. At the same time, the choice of algorithms, terminology, notation, and interpretation of Bayesian inference varies from one sub-field of astronomy to the next, which can lead to confusion to both those learning and those familiar with Bayesian statistics. Moreover, the choice varies between the astronomy and statistics literature, too. In this paper, our goal is two-fold: (1) provide a reference that consolidates and clarifies terminology and notation across disciplines, and (2) outline practical guidance for Baye"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.04703","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/2302.04703/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":"2302.04703","created_at":"2026-07-05T05:40:17.625928+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.04703v1","created_at":"2026-07-05T05:40:17.625928+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.04703","created_at":"2026-07-05T05:40:17.625928+00:00"},{"alias_kind":"pith_short_12","alias_value":"GIS6BFJXMN7Z","created_at":"2026-07-05T05:40:17.625928+00:00"},{"alias_kind":"pith_short_16","alias_value":"GIS6BFJXMN7ZA7YF","created_at":"2026-07-05T05:40:17.625928+00:00"},{"alias_kind":"pith_short_8","alias_value":"GIS6BFJX","created_at":"2026-07-05T05:40:17.625928+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.28489","citing_title":"pop-cosmos: Galaxy size evolution across structural and star-formation classifications in COSMOS-Web","ref_index":61,"is_internal_anchor":false},{"citing_arxiv_id":"2606.11308","citing_title":"pop-cosmos: Disentangling galaxy properties from observables using data-driven approaches","ref_index":59,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17377","citing_title":"Precise and Rapid Parameter Inference of Kilonova with Conditional Variational Autoencoder","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GIS6BFJXMN7ZA7YFG4WO6S5MVJ","json":"https://pith.science/pith/GIS6BFJXMN7ZA7YFG4WO6S5MVJ.json","graph_json":"https://pith.science/api/pith-number/GIS6BFJXMN7ZA7YFG4WO6S5MVJ/graph.json","events_json":"https://pith.science/api/pith-number/GIS6BFJXMN7ZA7YFG4WO6S5MVJ/events.json","paper":"https://pith.science/paper/GIS6BFJX"},"agent_actions":{"view_html":"https://pith.science/pith/GIS6BFJXMN7ZA7YFG4WO6S5MVJ","download_json":"https://pith.science/pith/GIS6BFJXMN7ZA7YFG4WO6S5MVJ.json","view_paper":"https://pith.science/paper/GIS6BFJX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.04703&json=true","fetch_graph":"https://pith.science/api/pith-number/GIS6BFJXMN7ZA7YFG4WO6S5MVJ/graph.json","fetch_events":"https://pith.science/api/pith-number/GIS6BFJXMN7ZA7YFG4WO6S5MVJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GIS6BFJXMN7ZA7YFG4WO6S5MVJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GIS6BFJXMN7ZA7YFG4WO6S5MVJ/action/storage_attestation","attest_author":"https://pith.science/pith/GIS6BFJXMN7ZA7YFG4WO6S5MVJ/action/author_attestation","sign_citation":"https://pith.science/pith/GIS6BFJXMN7ZA7YFG4WO6S5MVJ/action/citation_signature","submit_replication":"https://pith.science/pith/GIS6BFJXMN7ZA7YFG4WO6S5MVJ/action/replication_record"}},"created_at":"2026-07-05T05:40:17.625928+00:00","updated_at":"2026-07-05T05:40:17.625928+00:00"}