{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OF373FNDVCRY5XYEMQEGOINIOI","short_pith_number":"pith:OF373FND","schema_version":"1.0","canonical_sha256":"7177fd95a3a8a38edf0464086721a87207ddb71357589a537a7507d3a44c769e","source":{"kind":"arxiv","id":"2401.14868","version":1},"attestation_state":"computed","paper":{"title":"Particle-MALA and Particle-mGRAD: Gradient-based MCMC methods for high-dimensional state-space models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.CO","authors_text":"Adrien Corenflos, Axel Finke","submitted_at":"2024-01-26T13:52:40Z","abstract_excerpt":"State-of-the-art methods for Bayesian inference in state-space models are (a) conditional sequential Monte Carlo (CSMC) algorithms; (b) sophisticated 'classical' MCMC algorithms like MALA, or mGRAD from Titsias and Papaspiliopoulos (2018, arXiv:1610.09641v3 [stat.ML]). The former propose $N$ particles at each time step to exploit the model's 'decorrelation-over-time' property and thus scale favourably with the time horizon, $T$ , but break down if the dimension of the latent states, $D$, is large. The latter leverage gradient-/prior-informed local proposals to scale favourably with $D$ but exh"},"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":"2401.14868","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2024-01-26T13:52:40Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"056a1c5cb5f789dc1a22f30ba1759fc1f5b340fd41cba6cc0fe7e0257a921d29","abstract_canon_sha256":"c1177b20efe5b443b65ffdf711ecba0d2bac20446f499e0aeaa045f463e60890"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:38:02.942968Z","signature_b64":"jEGNq8dwWBGkrjYRNdz76LwwbwU8FoYOBh4uWmycwQhlSUPxof1i0vsAEUa0EHgNZmtN0dxpy9eCFIbhN50jCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7177fd95a3a8a38edf0464086721a87207ddb71357589a537a7507d3a44c769e","last_reissued_at":"2026-07-05T07:38:02.942523Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:38:02.942523Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Particle-MALA and Particle-mGRAD: Gradient-based MCMC methods for high-dimensional state-space models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.CO","authors_text":"Adrien Corenflos, Axel Finke","submitted_at":"2024-01-26T13:52:40Z","abstract_excerpt":"State-of-the-art methods for Bayesian inference in state-space models are (a) conditional sequential Monte Carlo (CSMC) algorithms; (b) sophisticated 'classical' MCMC algorithms like MALA, or mGRAD from Titsias and Papaspiliopoulos (2018, arXiv:1610.09641v3 [stat.ML]). The former propose $N$ particles at each time step to exploit the model's 'decorrelation-over-time' property and thus scale favourably with the time horizon, $T$ , but break down if the dimension of the latent states, $D$, is large. The latter leverage gradient-/prior-informed local proposals to scale favourably with $D$ but exh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.14868","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2401.14868/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2401.14868","created_at":"2026-07-05T07:38:02.942574+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.14868v1","created_at":"2026-07-05T07:38:02.942574+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.14868","created_at":"2026-07-05T07:38:02.942574+00:00"},{"alias_kind":"pith_short_12","alias_value":"OF373FNDVCRY","created_at":"2026-07-05T07:38:02.942574+00:00"},{"alias_kind":"pith_short_16","alias_value":"OF373FNDVCRY5XYE","created_at":"2026-07-05T07:38:02.942574+00:00"},{"alias_kind":"pith_short_8","alias_value":"OF373FND","created_at":"2026-07-05T07:38:02.942574+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.19743","citing_title":"A Bayesian spatio-temporal nearest neighbor Gaussian process model for pooled genetic data","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2409.14585","citing_title":"A convergent scheme for the Bayesian filtering problem based on the Fokker--Planck equation and deep splitting","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2508.10630","citing_title":"Nonlinear filtering based on density approximation and deep BSDE prediction","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OF373FNDVCRY5XYEMQEGOINIOI","json":"https://pith.science/pith/OF373FNDVCRY5XYEMQEGOINIOI.json","graph_json":"https://pith.science/api/pith-number/OF373FNDVCRY5XYEMQEGOINIOI/graph.json","events_json":"https://pith.science/api/pith-number/OF373FNDVCRY5XYEMQEGOINIOI/events.json","paper":"https://pith.science/paper/OF373FND"},"agent_actions":{"view_html":"https://pith.science/pith/OF373FNDVCRY5XYEMQEGOINIOI","download_json":"https://pith.science/pith/OF373FNDVCRY5XYEMQEGOINIOI.json","view_paper":"https://pith.science/paper/OF373FND","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.14868&json=true","fetch_graph":"https://pith.science/api/pith-number/OF373FNDVCRY5XYEMQEGOINIOI/graph.json","fetch_events":"https://pith.science/api/pith-number/OF373FNDVCRY5XYEMQEGOINIOI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OF373FNDVCRY5XYEMQEGOINIOI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OF373FNDVCRY5XYEMQEGOINIOI/action/storage_attestation","attest_author":"https://pith.science/pith/OF373FNDVCRY5XYEMQEGOINIOI/action/author_attestation","sign_citation":"https://pith.science/pith/OF373FNDVCRY5XYEMQEGOINIOI/action/citation_signature","submit_replication":"https://pith.science/pith/OF373FNDVCRY5XYEMQEGOINIOI/action/replication_record"}},"created_at":"2026-07-05T07:38:02.942574+00:00","updated_at":"2026-07-05T07:38:02.942574+00:00"}