{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:UCCOY73HMUKE7XBQBTW62W3M2Y","short_pith_number":"pith:UCCOY73H","schema_version":"1.0","canonical_sha256":"a084ec7f6765144fdc300ceded5b6cd6033b7b35b4fd583a392b306684f1e0a0","source":{"kind":"arxiv","id":"1401.6128","version":2},"attestation_state":"computed","paper":{"title":"The Probabilities of Orbital-Companion Models for Stellar Radial Velocity Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.EP"],"primary_cat":"astro-ph.IM","authors_text":"David W. Hogg, Fengji Hou, Jonathan Goodman","submitted_at":"2014-01-23T19:08:33Z","abstract_excerpt":"The fully marginalized likelihood, or Bayesian evidence, is of great importance in probabilistic data analysis, because it is involved in calculating the posterior probability of a model or re-weighting a mixture of models conditioned on data. It is, however, extremely challenging to compute. This paper presents a geometric-path Monte Carlo method, inspired by multi-canonical Monte Carlo to evaluate the fully marginalized likelihood. We show that the algorithm is very fast and easy to implement and produces a justified uncertainty estimate on the fully marginalized likelihood. The algorithm pe"},"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":"1401.6128","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2014-01-23T19:08:33Z","cross_cats_sorted":["astro-ph.EP"],"title_canon_sha256":"d9f737139df6533c8ceff610b1230c6aaa48d6aa01b9fb91890c56f4ea1719c8","abstract_canon_sha256":"951f92842086e4aa86689652d704e549cdcfd0ce40bdf34f3126ad89c1e65718"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:00:48.800707Z","signature_b64":"8jVZ5dYs6DIg1ypPcYk0TI5z9tUAoZIRS/wKHDtCQd2O4MZKYDQffwcNQQfHqqcoaDs19TkwCVj0sz8YHNviCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a084ec7f6765144fdc300ceded5b6cd6033b7b35b4fd583a392b306684f1e0a0","last_reissued_at":"2026-05-18T03:00:48.799976Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:00:48.799976Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Probabilities of Orbital-Companion Models for Stellar Radial Velocity Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.EP"],"primary_cat":"astro-ph.IM","authors_text":"David W. Hogg, Fengji Hou, Jonathan Goodman","submitted_at":"2014-01-23T19:08:33Z","abstract_excerpt":"The fully marginalized likelihood, or Bayesian evidence, is of great importance in probabilistic data analysis, because it is involved in calculating the posterior probability of a model or re-weighting a mixture of models conditioned on data. It is, however, extremely challenging to compute. This paper presents a geometric-path Monte Carlo method, inspired by multi-canonical Monte Carlo to evaluate the fully marginalized likelihood. We show that the algorithm is very fast and easy to implement and produces a justified uncertainty estimate on the fully marginalized likelihood. The algorithm pe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1401.6128","kind":"arxiv","version":2},"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":"1401.6128","created_at":"2026-05-18T03:00:48.800085+00:00"},{"alias_kind":"arxiv_version","alias_value":"1401.6128v2","created_at":"2026-05-18T03:00:48.800085+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1401.6128","created_at":"2026-05-18T03:00:48.800085+00:00"},{"alias_kind":"pith_short_12","alias_value":"UCCOY73HMUKE","created_at":"2026-05-18T12:28:52.271510+00:00"},{"alias_kind":"pith_short_16","alias_value":"UCCOY73HMUKE7XBQ","created_at":"2026-05-18T12:28:52.271510+00:00"},{"alias_kind":"pith_short_8","alias_value":"UCCOY73H","created_at":"2026-05-18T12:28:52.271510+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/UCCOY73HMUKE7XBQBTW62W3M2Y","json":"https://pith.science/pith/UCCOY73HMUKE7XBQBTW62W3M2Y.json","graph_json":"https://pith.science/api/pith-number/UCCOY73HMUKE7XBQBTW62W3M2Y/graph.json","events_json":"https://pith.science/api/pith-number/UCCOY73HMUKE7XBQBTW62W3M2Y/events.json","paper":"https://pith.science/paper/UCCOY73H"},"agent_actions":{"view_html":"https://pith.science/pith/UCCOY73HMUKE7XBQBTW62W3M2Y","download_json":"https://pith.science/pith/UCCOY73HMUKE7XBQBTW62W3M2Y.json","view_paper":"https://pith.science/paper/UCCOY73H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1401.6128&json=true","fetch_graph":"https://pith.science/api/pith-number/UCCOY73HMUKE7XBQBTW62W3M2Y/graph.json","fetch_events":"https://pith.science/api/pith-number/UCCOY73HMUKE7XBQBTW62W3M2Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UCCOY73HMUKE7XBQBTW62W3M2Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UCCOY73HMUKE7XBQBTW62W3M2Y/action/storage_attestation","attest_author":"https://pith.science/pith/UCCOY73HMUKE7XBQBTW62W3M2Y/action/author_attestation","sign_citation":"https://pith.science/pith/UCCOY73HMUKE7XBQBTW62W3M2Y/action/citation_signature","submit_replication":"https://pith.science/pith/UCCOY73HMUKE7XBQBTW62W3M2Y/action/replication_record"}},"created_at":"2026-05-18T03:00:48.800085+00:00","updated_at":"2026-05-18T03:00:48.800085+00:00"}