{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:DXFEDTVUE27OYGPWORYF4SSXZ2","short_pith_number":"pith:DXFEDTVU","schema_version":"1.0","canonical_sha256":"1dca41ceb426beec19f674705e4a57ce8c7c83996b4a0eb2f17f8478a27f8b95","source":{"kind":"arxiv","id":"1806.03120","version":1},"attestation_state":"computed","paper":{"title":"Variational inference for sparse network reconstruction from count data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Julien Chiquet, Mahendra Mariadassou, St\\'ephane Robin","submitted_at":"2018-06-08T12:38:21Z","abstract_excerpt":"In multivariate statistics, the question of finding direct interactions can be formulated as a problem of network inference - or network reconstruction - for which the Gaussian graphical model (GGM) provides a canonical framework. Unfortunately, the Gaussian assumption does not apply to count data which are encountered in domains such as genomics, social sciences or ecology.\n  To circumvent this limitation, state-of-the-art approaches use two-step strategies that first transform counts to pseudo Gaussian observations and then apply a (partial) correlation-based approach from the abundant liter"},"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":"1806.03120","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-06-08T12:38:21Z","cross_cats_sorted":[],"title_canon_sha256":"f5023f9cf7a9c65170363c2b1cbfde72969506e7b8f504e3c28e9b4c625d48e6","abstract_canon_sha256":"aa5cd51cb1442877ff76a701f811d88493300c82fa30ee42589821198af0c4fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:49.587699Z","signature_b64":"r+SKp5NtNWVhP4uZpbcPL4NtloglyLIzGtb8p0qo9VmB+hXChNI187zed8a5kMbdboMQrh4/nWbrwiMd5LWZCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1dca41ceb426beec19f674705e4a57ce8c7c83996b4a0eb2f17f8478a27f8b95","last_reissued_at":"2026-05-18T00:13:49.587045Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:49.587045Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Variational inference for sparse network reconstruction from count data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Julien Chiquet, Mahendra Mariadassou, St\\'ephane Robin","submitted_at":"2018-06-08T12:38:21Z","abstract_excerpt":"In multivariate statistics, the question of finding direct interactions can be formulated as a problem of network inference - or network reconstruction - for which the Gaussian graphical model (GGM) provides a canonical framework. Unfortunately, the Gaussian assumption does not apply to count data which are encountered in domains such as genomics, social sciences or ecology.\n  To circumvent this limitation, state-of-the-art approaches use two-step strategies that first transform counts to pseudo Gaussian observations and then apply a (partial) correlation-based approach from the abundant liter"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.03120","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":"1806.03120","created_at":"2026-05-18T00:13:49.587129+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.03120v1","created_at":"2026-05-18T00:13:49.587129+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.03120","created_at":"2026-05-18T00:13:49.587129+00:00"},{"alias_kind":"pith_short_12","alias_value":"DXFEDTVUE27O","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"DXFEDTVUE27OYGPW","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"DXFEDTVU","created_at":"2026-05-18T12:32:19.392346+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/DXFEDTVUE27OYGPWORYF4SSXZ2","json":"https://pith.science/pith/DXFEDTVUE27OYGPWORYF4SSXZ2.json","graph_json":"https://pith.science/api/pith-number/DXFEDTVUE27OYGPWORYF4SSXZ2/graph.json","events_json":"https://pith.science/api/pith-number/DXFEDTVUE27OYGPWORYF4SSXZ2/events.json","paper":"https://pith.science/paper/DXFEDTVU"},"agent_actions":{"view_html":"https://pith.science/pith/DXFEDTVUE27OYGPWORYF4SSXZ2","download_json":"https://pith.science/pith/DXFEDTVUE27OYGPWORYF4SSXZ2.json","view_paper":"https://pith.science/paper/DXFEDTVU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.03120&json=true","fetch_graph":"https://pith.science/api/pith-number/DXFEDTVUE27OYGPWORYF4SSXZ2/graph.json","fetch_events":"https://pith.science/api/pith-number/DXFEDTVUE27OYGPWORYF4SSXZ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DXFEDTVUE27OYGPWORYF4SSXZ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DXFEDTVUE27OYGPWORYF4SSXZ2/action/storage_attestation","attest_author":"https://pith.science/pith/DXFEDTVUE27OYGPWORYF4SSXZ2/action/author_attestation","sign_citation":"https://pith.science/pith/DXFEDTVUE27OYGPWORYF4SSXZ2/action/citation_signature","submit_replication":"https://pith.science/pith/DXFEDTVUE27OYGPWORYF4SSXZ2/action/replication_record"}},"created_at":"2026-05-18T00:13:49.587129+00:00","updated_at":"2026-05-18T00:13:49.587129+00:00"}