{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:MSZ6QGSEKLZYU26TROQJLPHOPZ","short_pith_number":"pith:MSZ6QGSE","schema_version":"1.0","canonical_sha256":"64b3e81a4452f38a6bd38ba095bcee7e6684afa2e67e5548c6966e2361a66da5","source":{"kind":"arxiv","id":"1506.04000","version":1},"attestation_state":"computed","paper":{"title":"MCMC for Variationally Sparse Gaussian Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Alexander G. de G. Matthews, James Hensman, Maurizio Filippone, Zoubin Ghahramani","submitted_at":"2015-06-12T12:24:35Z","abstract_excerpt":"Gaussian process (GP) models form a core part of probabilistic machine learning. Considerable research effort has been made into attacking three issues with GP models: how to compute efficiently when the number of data is large; how to approximate the posterior when the likelihood is not Gaussian and how to estimate covariance function parameter posteriors. This paper simultaneously addresses these, using a variational approximation to the posterior which is sparse in support of the function but otherwise free-form. The result is a Hybrid Monte-Carlo sampling scheme which allows for a non-Gaus"},"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":"1506.04000","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-06-12T12:24:35Z","cross_cats_sorted":[],"title_canon_sha256":"3363703e80fea752f48f45dadbbc4b4335d2d35208a3b6c80f381546cd1c4b6a","abstract_canon_sha256":"626a817c69ae1c1b3da860583e015071dbc5b480ed5ef8f97f8e2e16f551f260"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:51:18.568142Z","signature_b64":"rEgmTXw3nQ7BPRZ6Vz1exBDGVTgdbX2JxZTkqbD4xBmW0oyS0fpdaRdGCU5++uy9jxr1tVsZeHzqHj0GF08qDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64b3e81a4452f38a6bd38ba095bcee7e6684afa2e67e5548c6966e2361a66da5","last_reissued_at":"2026-05-18T01:51:18.567547Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:51:18.567547Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MCMC for Variationally Sparse Gaussian Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Alexander G. de G. Matthews, James Hensman, Maurizio Filippone, Zoubin Ghahramani","submitted_at":"2015-06-12T12:24:35Z","abstract_excerpt":"Gaussian process (GP) models form a core part of probabilistic machine learning. Considerable research effort has been made into attacking three issues with GP models: how to compute efficiently when the number of data is large; how to approximate the posterior when the likelihood is not Gaussian and how to estimate covariance function parameter posteriors. This paper simultaneously addresses these, using a variational approximation to the posterior which is sparse in support of the function but otherwise free-form. The result is a Hybrid Monte-Carlo sampling scheme which allows for a non-Gaus"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.04000","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":"1506.04000","created_at":"2026-05-18T01:51:18.567636+00:00"},{"alias_kind":"arxiv_version","alias_value":"1506.04000v1","created_at":"2026-05-18T01:51:18.567636+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.04000","created_at":"2026-05-18T01:51:18.567636+00:00"},{"alias_kind":"pith_short_12","alias_value":"MSZ6QGSEKLZY","created_at":"2026-05-18T12:29:32.376354+00:00"},{"alias_kind":"pith_short_16","alias_value":"MSZ6QGSEKLZYU26T","created_at":"2026-05-18T12:29:32.376354+00:00"},{"alias_kind":"pith_short_8","alias_value":"MSZ6QGSE","created_at":"2026-05-18T12:29:32.376354+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/MSZ6QGSEKLZYU26TROQJLPHOPZ","json":"https://pith.science/pith/MSZ6QGSEKLZYU26TROQJLPHOPZ.json","graph_json":"https://pith.science/api/pith-number/MSZ6QGSEKLZYU26TROQJLPHOPZ/graph.json","events_json":"https://pith.science/api/pith-number/MSZ6QGSEKLZYU26TROQJLPHOPZ/events.json","paper":"https://pith.science/paper/MSZ6QGSE"},"agent_actions":{"view_html":"https://pith.science/pith/MSZ6QGSEKLZYU26TROQJLPHOPZ","download_json":"https://pith.science/pith/MSZ6QGSEKLZYU26TROQJLPHOPZ.json","view_paper":"https://pith.science/paper/MSZ6QGSE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1506.04000&json=true","fetch_graph":"https://pith.science/api/pith-number/MSZ6QGSEKLZYU26TROQJLPHOPZ/graph.json","fetch_events":"https://pith.science/api/pith-number/MSZ6QGSEKLZYU26TROQJLPHOPZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MSZ6QGSEKLZYU26TROQJLPHOPZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MSZ6QGSEKLZYU26TROQJLPHOPZ/action/storage_attestation","attest_author":"https://pith.science/pith/MSZ6QGSEKLZYU26TROQJLPHOPZ/action/author_attestation","sign_citation":"https://pith.science/pith/MSZ6QGSEKLZYU26TROQJLPHOPZ/action/citation_signature","submit_replication":"https://pith.science/pith/MSZ6QGSEKLZYU26TROQJLPHOPZ/action/replication_record"}},"created_at":"2026-05-18T01:51:18.567636+00:00","updated_at":"2026-05-18T01:51:18.567636+00:00"}