{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:EZ5MFNSQ2QCY6O2UXVSEWHFBVG","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"49fd777311c03f704af2a6b606fb0d05b83de0a640df367c62da7782502c4eb8","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2024-06-07T09:48:11Z","title_canon_sha256":"a08811d0ef2d4364d82995a9ba6a1bf4b52f56f33b8ec3e5c990ecf8a070cd71"},"schema_version":"1.0","source":{"id":"2406.04796","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.04796","created_at":"2026-07-05T08:28:49Z"},{"alias_kind":"arxiv_version","alias_value":"2406.04796v1","created_at":"2026-07-05T08:28:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.04796","created_at":"2026-07-05T08:28:49Z"},{"alias_kind":"pith_short_12","alias_value":"EZ5MFNSQ2QCY","created_at":"2026-07-05T08:28:49Z"},{"alias_kind":"pith_short_16","alias_value":"EZ5MFNSQ2QCY6O2U","created_at":"2026-07-05T08:28:49Z"},{"alias_kind":"pith_short_8","alias_value":"EZ5MFNSQ","created_at":"2026-07-05T08:28:49Z"}],"graph_snapshots":[{"event_id":"sha256:cb917ab7984eb71f3617fdee8609523f80a586e6744e0a736d151bbb0a1aecc2","target":"graph","created_at":"2026-07-05T08:28:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2406.04796/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Several disciplines, such as econometrics, neuroscience, and computational psychology, study the dynamic interactions between variables over time. A Bayesian nonparametric model known as the Wishart process has been shown to be effective in this situation, but its inference remains highly challenging. In this work, we introduce a Sequential Monte Carlo (SMC) sampler for the Wishart process, and show how it compares to conventional inference approaches, namely MCMC and variational inference. Using simulations we show that SMC sampling results in the most robust estimates and out-of-sample predi","authors_text":"David Leeftink, Hester Huijsdens, Linda Geerligs, Max Hinne","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2024-06-07T09:48:11Z","title":"Robust Inference of Dynamic Covariance Using Wishart Processes and Sequential Monte Carlo"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.04796","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:599d968df854897791f9b191506fe102c38d2488a1a7ff2d829d4ece82f52169","target":"record","created_at":"2026-07-05T08:28:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"49fd777311c03f704af2a6b606fb0d05b83de0a640df367c62da7782502c4eb8","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2024-06-07T09:48:11Z","title_canon_sha256":"a08811d0ef2d4364d82995a9ba6a1bf4b52f56f33b8ec3e5c990ecf8a070cd71"},"schema_version":"1.0","source":{"id":"2406.04796","kind":"arxiv","version":1}},"canonical_sha256":"267ac2b650d4058f3b54bd644b1ca1a9a9fbab984fe3d1b1686d9afa1a436122","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"267ac2b650d4058f3b54bd644b1ca1a9a9fbab984fe3d1b1686d9afa1a436122","first_computed_at":"2026-07-05T08:28:49.413143Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:28:49.413143Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zm5yvDziF8P8n4IdgcxLcZmW3tJgJmnBAHsN1Lgk9Ii1ScCqDYoaIu/jSlkcfW57kgf8bq17U4Av39T0v97RBA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:28:49.413624Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.04796","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:599d968df854897791f9b191506fe102c38d2488a1a7ff2d829d4ece82f52169","sha256:cb917ab7984eb71f3617fdee8609523f80a586e6744e0a736d151bbb0a1aecc2"],"state_sha256":"e249ff301b24f3b3a8bebcdc9d1a0a404c5237a5132a8c5ea81e80b9658d1908"}