{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:D5VRMUFLO4MRXQDFMOHQ36MWLZ","short_pith_number":"pith:D5VRMUFL","schema_version":"1.0","canonical_sha256":"1f6b1650ab77191bc065638f0df9965e601b1e6d88b659d098d33ed4094f9700","source":{"kind":"arxiv","id":"2011.02328","version":2},"attestation_state":"computed","paper":{"title":"Waste-free Sequential Monte Carlo","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Hai-Dang Dau, Nicolas Chopin","submitted_at":"2020-11-04T14:58:29Z","abstract_excerpt":"A standard way to move particles in a SMC sampler is to apply several steps of a MCMC (Markov chain Monte Carlo) kernel. Unfortunately, it is not clear how many steps need to be performed for optimal performance. In addition, the output of the intermediate steps are discarded and thus wasted somehow. We propose a new, waste-free SMC algorithm which uses the outputs of all these intermediate MCMC steps as particles. We establish that its output is consistent and asymptotically normal. We use the expression of the asymptotic variance to develop various insights on how to implement the algorithm "},"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":"2011.02328","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2020-11-04T14:58:29Z","cross_cats_sorted":[],"title_canon_sha256":"7e58febad1fa4d2a813a8d968896156c3716b69201ecc70b94b6deb45078466e","abstract_canon_sha256":"9002dbde240fb0d9eb762b8b991fad761e690d3d9218271943709c0632e658e7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:07:44.818554Z","signature_b64":"y8ym5CQ5fHbYkaPFfbLVZtXkRyUyls8MzoudjPpkhSM6hiImiHgE8xa8/V55kbNQtzqwSNIFAkpLN+Q6g1duDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f6b1650ab77191bc065638f0df9965e601b1e6d88b659d098d33ed4094f9700","last_reissued_at":"2026-07-05T03:07:44.818204Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:07:44.818204Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Waste-free Sequential Monte Carlo","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Hai-Dang Dau, Nicolas Chopin","submitted_at":"2020-11-04T14:58:29Z","abstract_excerpt":"A standard way to move particles in a SMC sampler is to apply several steps of a MCMC (Markov chain Monte Carlo) kernel. Unfortunately, it is not clear how many steps need to be performed for optimal performance. In addition, the output of the intermediate steps are discarded and thus wasted somehow. We propose a new, waste-free SMC algorithm which uses the outputs of all these intermediate MCMC steps as particles. We establish that its output is consistent and asymptotically normal. We use the expression of the asymptotic variance to develop various insights on how to implement the algorithm "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.02328","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2011.02328/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":"2011.02328","created_at":"2026-07-05T03:07:44.818260+00:00"},{"alias_kind":"arxiv_version","alias_value":"2011.02328v2","created_at":"2026-07-05T03:07:44.818260+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.02328","created_at":"2026-07-05T03:07:44.818260+00:00"},{"alias_kind":"pith_short_12","alias_value":"D5VRMUFLO4MR","created_at":"2026-07-05T03:07:44.818260+00:00"},{"alias_kind":"pith_short_16","alias_value":"D5VRMUFLO4MRXQDF","created_at":"2026-07-05T03:07:44.818260+00:00"},{"alias_kind":"pith_short_8","alias_value":"D5VRMUFL","created_at":"2026-07-05T03:07:44.818260+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.04336","citing_title":"Gravitational-wave inference at GPU speed: A bilby-like nested sampling kernel within blackjax-ns","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/D5VRMUFLO4MRXQDFMOHQ36MWLZ","json":"https://pith.science/pith/D5VRMUFLO4MRXQDFMOHQ36MWLZ.json","graph_json":"https://pith.science/api/pith-number/D5VRMUFLO4MRXQDFMOHQ36MWLZ/graph.json","events_json":"https://pith.science/api/pith-number/D5VRMUFLO4MRXQDFMOHQ36MWLZ/events.json","paper":"https://pith.science/paper/D5VRMUFL"},"agent_actions":{"view_html":"https://pith.science/pith/D5VRMUFLO4MRXQDFMOHQ36MWLZ","download_json":"https://pith.science/pith/D5VRMUFLO4MRXQDFMOHQ36MWLZ.json","view_paper":"https://pith.science/paper/D5VRMUFL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2011.02328&json=true","fetch_graph":"https://pith.science/api/pith-number/D5VRMUFLO4MRXQDFMOHQ36MWLZ/graph.json","fetch_events":"https://pith.science/api/pith-number/D5VRMUFLO4MRXQDFMOHQ36MWLZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D5VRMUFLO4MRXQDFMOHQ36MWLZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D5VRMUFLO4MRXQDFMOHQ36MWLZ/action/storage_attestation","attest_author":"https://pith.science/pith/D5VRMUFLO4MRXQDFMOHQ36MWLZ/action/author_attestation","sign_citation":"https://pith.science/pith/D5VRMUFLO4MRXQDFMOHQ36MWLZ/action/citation_signature","submit_replication":"https://pith.science/pith/D5VRMUFLO4MRXQDFMOHQ36MWLZ/action/replication_record"}},"created_at":"2026-07-05T03:07:44.818260+00:00","updated_at":"2026-07-05T03:07:44.818260+00:00"}