{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2010:445CXWV7XBZQG5VU4ZNIJMOTLB","short_pith_number":"pith:445CXWV7","schema_version":"1.0","canonical_sha256":"e73a2bdabfb8730376b4e65a84b1d35850481f171ca320b738f919f23ea1c665","source":{"kind":"arxiv","id":"1003.1136","version":1},"attestation_state":"computed","paper":{"title":"Analytic Methods for Cosmological Likelihoods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"A. N. Taylor, T. D. Kitching","submitted_at":"2010-03-04T21:09:07Z","abstract_excerpt":"We present general, analytic methods for Cosmological likelihood analysis and solve the \"many-parameters\" problem in Cosmology. Maxima are found by Newton's Method, while marginalization over nuisance parameters, and parameter errors and covariances are estimated by analytic marginalization of an arbitrary likelihood function with flat or Gaussian priors. We show that information about remaining parameters is preserved by marginalization. Marginalizing over all parameters, we find an analytic expression for the Bayesian evidence for model selection. We apply these methods to data described by "},"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":"1003.1136","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2010-03-04T21:09:07Z","cross_cats_sorted":[],"title_canon_sha256":"a34b76187c10384769c835f4a88a5230f8a5284aa541b1b5f293d24db5e21b70","abstract_canon_sha256":"605f25bae179b25f75acdc93e033d1d4119aa2063eefe7eada3ce817e988df57"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:08:46.118373Z","signature_b64":"fQmfPq6VSnvAengzmJAvN/OOsR++wnzqqQr9arA/tzWGlpfgCaQx1bT+ffZzxY66UmsfQ/bT6CKnBOOtNIBmCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e73a2bdabfb8730376b4e65a84b1d35850481f171ca320b738f919f23ea1c665","last_reissued_at":"2026-05-18T02:08:46.117411Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:08:46.117411Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Analytic Methods for Cosmological Likelihoods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"A. N. Taylor, T. D. Kitching","submitted_at":"2010-03-04T21:09:07Z","abstract_excerpt":"We present general, analytic methods for Cosmological likelihood analysis and solve the \"many-parameters\" problem in Cosmology. Maxima are found by Newton's Method, while marginalization over nuisance parameters, and parameter errors and covariances are estimated by analytic marginalization of an arbitrary likelihood function with flat or Gaussian priors. We show that information about remaining parameters is preserved by marginalization. Marginalizing over all parameters, we find an analytic expression for the Bayesian evidence for model selection. We apply these methods to data described by "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1003.1136","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":"1003.1136","created_at":"2026-05-18T02:08:46.117581+00:00"},{"alias_kind":"arxiv_version","alias_value":"1003.1136v1","created_at":"2026-05-18T02:08:46.117581+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1003.1136","created_at":"2026-05-18T02:08:46.117581+00:00"},{"alias_kind":"pith_short_12","alias_value":"445CXWV7XBZQ","created_at":"2026-05-18T12:26:03.138858+00:00"},{"alias_kind":"pith_short_16","alias_value":"445CXWV7XBZQG5VU","created_at":"2026-05-18T12:26:03.138858+00:00"},{"alias_kind":"pith_short_8","alias_value":"445CXWV7","created_at":"2026-05-18T12:26:03.138858+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.26915","citing_title":"Testing Scale-Dependent Modified Gravity with DESI DR1","ref_index":122,"is_internal_anchor":false},{"citing_arxiv_id":"2604.09407","citing_title":"Analytic compression of the effective field theory of the Lyman-alpha forest","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/445CXWV7XBZQG5VU4ZNIJMOTLB","json":"https://pith.science/pith/445CXWV7XBZQG5VU4ZNIJMOTLB.json","graph_json":"https://pith.science/api/pith-number/445CXWV7XBZQG5VU4ZNIJMOTLB/graph.json","events_json":"https://pith.science/api/pith-number/445CXWV7XBZQG5VU4ZNIJMOTLB/events.json","paper":"https://pith.science/paper/445CXWV7"},"agent_actions":{"view_html":"https://pith.science/pith/445CXWV7XBZQG5VU4ZNIJMOTLB","download_json":"https://pith.science/pith/445CXWV7XBZQG5VU4ZNIJMOTLB.json","view_paper":"https://pith.science/paper/445CXWV7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1003.1136&json=true","fetch_graph":"https://pith.science/api/pith-number/445CXWV7XBZQG5VU4ZNIJMOTLB/graph.json","fetch_events":"https://pith.science/api/pith-number/445CXWV7XBZQG5VU4ZNIJMOTLB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/445CXWV7XBZQG5VU4ZNIJMOTLB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/445CXWV7XBZQG5VU4ZNIJMOTLB/action/storage_attestation","attest_author":"https://pith.science/pith/445CXWV7XBZQG5VU4ZNIJMOTLB/action/author_attestation","sign_citation":"https://pith.science/pith/445CXWV7XBZQG5VU4ZNIJMOTLB/action/citation_signature","submit_replication":"https://pith.science/pith/445CXWV7XBZQG5VU4ZNIJMOTLB/action/replication_record"}},"created_at":"2026-05-18T02:08:46.117581+00:00","updated_at":"2026-05-18T02:08:46.117581+00:00"}