{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:SCKONX6BHDTPVP2WZRVXKMWTJY","short_pith_number":"pith:SCKONX6B","schema_version":"1.0","canonical_sha256":"9094e6dfc138e6fabf56cc6b7532d34e3af0aefe29f8d5c5e0c820245dc460a5","source":{"kind":"arxiv","id":"1506.07564","version":2},"attestation_state":"computed","paper":{"title":"Spectral likelihood expansions for Bayesian inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Bruno Sudret, Joseph B. Nagel","submitted_at":"2015-06-24T21:11:48Z","abstract_excerpt":"A spectral approach to Bayesian inference is presented. It pursues the emulation of the posterior probability density. The starting point is a series expansion of the likelihood function in terms of orthogonal polynomials. From this spectral likelihood expansion all statistical quantities of interest can be calculated semi-analytically. The posterior is formally represented as the product of a reference density and a linear combination of polynomial basis functions. Both the model evidence and the posterior moments are related to the expansion coefficients. This formulation avoids Markov chain"},"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":"1506.07564","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-06-24T21:11:48Z","cross_cats_sorted":[],"title_canon_sha256":"f62a83eee61ba3fcac56433108d299e89a8eec43db5d7b11ad1074c8e292ea80","abstract_canon_sha256":"ae195e3b32c6b436b41283816dd61173cadf801baa7f8bc8a8f802bb62daa598"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:16:21.731489Z","signature_b64":"7IT5eosswdiCx9zDAn2EejCy4nwO7qEd5uPKGrVddKRwuLKd8No0O2b3dofY+b5Q3aBJM+DrAtvpqc9L01FiBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9094e6dfc138e6fabf56cc6b7532d34e3af0aefe29f8d5c5e0c820245dc460a5","last_reissued_at":"2026-05-18T01:16:21.730949Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:16:21.730949Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spectral likelihood expansions for Bayesian inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Bruno Sudret, Joseph B. Nagel","submitted_at":"2015-06-24T21:11:48Z","abstract_excerpt":"A spectral approach to Bayesian inference is presented. It pursues the emulation of the posterior probability density. The starting point is a series expansion of the likelihood function in terms of orthogonal polynomials. From this spectral likelihood expansion all statistical quantities of interest can be calculated semi-analytically. The posterior is formally represented as the product of a reference density and a linear combination of polynomial basis functions. Both the model evidence and the posterior moments are related to the expansion coefficients. This formulation avoids Markov chain"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.07564","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":"1506.07564","created_at":"2026-05-18T01:16:21.731014+00:00"},{"alias_kind":"arxiv_version","alias_value":"1506.07564v2","created_at":"2026-05-18T01:16:21.731014+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.07564","created_at":"2026-05-18T01:16:21.731014+00:00"},{"alias_kind":"pith_short_12","alias_value":"SCKONX6BHDTP","created_at":"2026-05-18T12:29:39.896362+00:00"},{"alias_kind":"pith_short_16","alias_value":"SCKONX6BHDTPVP2W","created_at":"2026-05-18T12:29:39.896362+00:00"},{"alias_kind":"pith_short_8","alias_value":"SCKONX6B","created_at":"2026-05-18T12:29:39.896362+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/SCKONX6BHDTPVP2WZRVXKMWTJY","json":"https://pith.science/pith/SCKONX6BHDTPVP2WZRVXKMWTJY.json","graph_json":"https://pith.science/api/pith-number/SCKONX6BHDTPVP2WZRVXKMWTJY/graph.json","events_json":"https://pith.science/api/pith-number/SCKONX6BHDTPVP2WZRVXKMWTJY/events.json","paper":"https://pith.science/paper/SCKONX6B"},"agent_actions":{"view_html":"https://pith.science/pith/SCKONX6BHDTPVP2WZRVXKMWTJY","download_json":"https://pith.science/pith/SCKONX6BHDTPVP2WZRVXKMWTJY.json","view_paper":"https://pith.science/paper/SCKONX6B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1506.07564&json=true","fetch_graph":"https://pith.science/api/pith-number/SCKONX6BHDTPVP2WZRVXKMWTJY/graph.json","fetch_events":"https://pith.science/api/pith-number/SCKONX6BHDTPVP2WZRVXKMWTJY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SCKONX6BHDTPVP2WZRVXKMWTJY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SCKONX6BHDTPVP2WZRVXKMWTJY/action/storage_attestation","attest_author":"https://pith.science/pith/SCKONX6BHDTPVP2WZRVXKMWTJY/action/author_attestation","sign_citation":"https://pith.science/pith/SCKONX6BHDTPVP2WZRVXKMWTJY/action/citation_signature","submit_replication":"https://pith.science/pith/SCKONX6BHDTPVP2WZRVXKMWTJY/action/replication_record"}},"created_at":"2026-05-18T01:16:21.731014+00:00","updated_at":"2026-05-18T01:16:21.731014+00:00"}