{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FXPFDOJG2J2FQ4Z6BYLVKQIY3K","short_pith_number":"pith:FXPFDOJG","schema_version":"1.0","canonical_sha256":"2dde51b926d27458733e0e17554118da9835873a863aa9e8a494e4a2b353c594","source":{"kind":"arxiv","id":"2506.06660","version":1},"attestation_state":"computed","paper":{"title":"Efficient Mirror-type Kernels for the Metropolis-Hastings Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Nuo Guan, Xiyun Jiao","submitted_at":"2025-06-07T04:48:45Z","abstract_excerpt":"We propose a new Metropolis-Hastings (MH) kernel by introducing the Mirror move into the Metropolis adjusted Langevin algorithm (MALA). This new kernel uses the strength of one kernel to overcome the shortcoming of the other, and generates proposals that are distant from the current position, but still within the high-density region of the target distribution. The resulting algorithm can be much more efficient than both Mirror and MALA, while stays comparable in terms of computational cost. We demonstrate the advantages of the MirrorMALA kernel using a variety of one-dimensional and multi-dime"},"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":"2506.06660","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2025-06-07T04:48:45Z","cross_cats_sorted":[],"title_canon_sha256":"4588487813f9084c5f111ff0c96fe5eaa0794b107dbc238e0321f463df0f3c72","abstract_canon_sha256":"d6c04becc845484d4f682aeed0b35953ff13e5b052d706d638bc4d7b2bf5eb49"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:53.531156Z","signature_b64":"SGt2H7D/+KDXxVkhYpLRWLtcrIh318YM9SXYBCnNk5FeY/vaUgqmz8j/vTg2Q0U/kiia4w1dXzAQf81AgC15CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2dde51b926d27458733e0e17554118da9835873a863aa9e8a494e4a2b353c594","last_reissued_at":"2026-07-05T11:17:53.530620Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:53.530620Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Mirror-type Kernels for the Metropolis-Hastings Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Nuo Guan, Xiyun Jiao","submitted_at":"2025-06-07T04:48:45Z","abstract_excerpt":"We propose a new Metropolis-Hastings (MH) kernel by introducing the Mirror move into the Metropolis adjusted Langevin algorithm (MALA). This new kernel uses the strength of one kernel to overcome the shortcoming of the other, and generates proposals that are distant from the current position, but still within the high-density region of the target distribution. The resulting algorithm can be much more efficient than both Mirror and MALA, while stays comparable in terms of computational cost. We demonstrate the advantages of the MirrorMALA kernel using a variety of one-dimensional and multi-dime"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06660","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/2506.06660/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":"2506.06660","created_at":"2026-07-05T11:17:53.530677+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.06660v1","created_at":"2026-07-05T11:17:53.530677+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06660","created_at":"2026-07-05T11:17:53.530677+00:00"},{"alias_kind":"pith_short_12","alias_value":"FXPFDOJG2J2F","created_at":"2026-07-05T11:17:53.530677+00:00"},{"alias_kind":"pith_short_16","alias_value":"FXPFDOJG2J2FQ4Z6","created_at":"2026-07-05T11:17:53.530677+00:00"},{"alias_kind":"pith_short_8","alias_value":"FXPFDOJG","created_at":"2026-07-05T11:17:53.530677+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/FXPFDOJG2J2FQ4Z6BYLVKQIY3K","json":"https://pith.science/pith/FXPFDOJG2J2FQ4Z6BYLVKQIY3K.json","graph_json":"https://pith.science/api/pith-number/FXPFDOJG2J2FQ4Z6BYLVKQIY3K/graph.json","events_json":"https://pith.science/api/pith-number/FXPFDOJG2J2FQ4Z6BYLVKQIY3K/events.json","paper":"https://pith.science/paper/FXPFDOJG"},"agent_actions":{"view_html":"https://pith.science/pith/FXPFDOJG2J2FQ4Z6BYLVKQIY3K","download_json":"https://pith.science/pith/FXPFDOJG2J2FQ4Z6BYLVKQIY3K.json","view_paper":"https://pith.science/paper/FXPFDOJG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.06660&json=true","fetch_graph":"https://pith.science/api/pith-number/FXPFDOJG2J2FQ4Z6BYLVKQIY3K/graph.json","fetch_events":"https://pith.science/api/pith-number/FXPFDOJG2J2FQ4Z6BYLVKQIY3K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FXPFDOJG2J2FQ4Z6BYLVKQIY3K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FXPFDOJG2J2FQ4Z6BYLVKQIY3K/action/storage_attestation","attest_author":"https://pith.science/pith/FXPFDOJG2J2FQ4Z6BYLVKQIY3K/action/author_attestation","sign_citation":"https://pith.science/pith/FXPFDOJG2J2FQ4Z6BYLVKQIY3K/action/citation_signature","submit_replication":"https://pith.science/pith/FXPFDOJG2J2FQ4Z6BYLVKQIY3K/action/replication_record"}},"created_at":"2026-07-05T11:17:53.530677+00:00","updated_at":"2026-07-05T11:17:53.530677+00:00"}