{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:T4P2SASE6GG7HFDS2X7JBPRNBM","short_pith_number":"pith:T4P2SASE","canonical_record":{"source":{"id":"2412.17899","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-12-23T19:00:02Z","cross_cats_sorted":["cs.DS","stat.ML","stat.TH"],"title_canon_sha256":"81901caf4ae2291a20b73523b6fd9ba21a32998634e288efc3e8c23cf1b3ef25","abstract_canon_sha256":"b45d4352ef37ead34d0f3424c4a50992414153448f9ca29d2ad0a3850704facb"},"schema_version":"1.0"},"canonical_sha256":"9f1fa90244f18df39472d5fe90be2d0b252a20ef441f2d11908dbbd86fd28ef6","source":{"kind":"arxiv","id":"2412.17899","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.17899","created_at":"2026-07-05T09:53:30Z"},{"alias_kind":"arxiv_version","alias_value":"2412.17899v1","created_at":"2026-07-05T09:53:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.17899","created_at":"2026-07-05T09:53:30Z"},{"alias_kind":"pith_short_12","alias_value":"T4P2SASE6GG7","created_at":"2026-07-05T09:53:30Z"},{"alias_kind":"pith_short_16","alias_value":"T4P2SASE6GG7HFDS","created_at":"2026-07-05T09:53:30Z"},{"alias_kind":"pith_short_8","alias_value":"T4P2SASE","created_at":"2026-07-05T09:53:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:T4P2SASE6GG7HFDS2X7JBPRNBM","target":"record","payload":{"canonical_record":{"source":{"id":"2412.17899","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-12-23T19:00:02Z","cross_cats_sorted":["cs.DS","stat.ML","stat.TH"],"title_canon_sha256":"81901caf4ae2291a20b73523b6fd9ba21a32998634e288efc3e8c23cf1b3ef25","abstract_canon_sha256":"b45d4352ef37ead34d0f3424c4a50992414153448f9ca29d2ad0a3850704facb"},"schema_version":"1.0"},"canonical_sha256":"9f1fa90244f18df39472d5fe90be2d0b252a20ef441f2d11908dbbd86fd28ef6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:53:30.583226Z","signature_b64":"ySWDtTB5SP+2Qmyq9fkwj1R9la5rbR4+oO8kEtjROQuwb/FHdx0ijxUa8HzpZTnhafOMJilDPO+T/Bg4iL1jBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f1fa90244f18df39472d5fe90be2d0b252a20ef441f2d11908dbbd86fd28ef6","last_reissued_at":"2026-07-05T09:53:30.582778Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:53:30.582778Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.17899","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:53:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ImxKYwEHahVzxzWhilwz3bsV/R+qjGKk4oM61MxRBs3wmgc2yMkgXyArnrj2Z1dCVDl0PFM2WZ2xbvQH2XOJDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-02T15:32:38.769246Z"},"content_sha256":"4c98c52516322d11d0b3f7d6d17e4e3627357c1d124c73b58753a7cfef7d2fbe","schema_version":"1.0","event_id":"sha256:4c98c52516322d11d0b3f7d6d17e4e3627357c1d124c73b58753a7cfef7d2fbe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:T4P2SASE6GG7HFDS2X7JBPRNBM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A mixing time bound for Gibbs sampling from log-smooth log-concave distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Neha S. Wadia","submitted_at":"2024-12-23T19:00:02Z","abstract_excerpt":"The Gibbs sampler, also known as the coordinate hit-and-run algorithm, is a Markov chain that is widely used to draw samples from probability distributions in arbitrary dimensions. At each iteration of the algorithm, a randomly selected coordinate is resampled from the distribution that results from conditioning on all the other coordinates. We study the behavior of the Gibbs sampler on the class of log-smooth and strongly log-concave target distributions supported on $\\mathbb{R}^n$. Assuming the initial distribution is $M$-warm with respect to the target, we show that the Gibbs sampler requir"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.17899","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/2412.17899/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:53:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wktHiXZKy450ow05onofbPWaMO7MJIB+qQO75BwmeJFE6xMrFBqS6W1X/UeONhqgeSqZu4vLlW/Is7DR6dcGDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-02T15:32:38.770138Z"},"content_sha256":"b40d2a86305a146c7c30b31ed5ff88828c42e430873017df587e39d64e153483","schema_version":"1.0","event_id":"sha256:b40d2a86305a146c7c30b31ed5ff88828c42e430873017df587e39d64e153483"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/T4P2SASE6GG7HFDS2X7JBPRNBM/bundle.json","state_url":"https://pith.science/pith/T4P2SASE6GG7HFDS2X7JBPRNBM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/T4P2SASE6GG7HFDS2X7JBPRNBM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-02T15:32:38Z","links":{"resolver":"https://pith.science/pith/T4P2SASE6GG7HFDS2X7JBPRNBM","bundle":"https://pith.science/pith/T4P2SASE6GG7HFDS2X7JBPRNBM/bundle.json","state":"https://pith.science/pith/T4P2SASE6GG7HFDS2X7JBPRNBM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/T4P2SASE6GG7HFDS2X7JBPRNBM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:T4P2SASE6GG7HFDS2X7JBPRNBM","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":"b45d4352ef37ead34d0f3424c4a50992414153448f9ca29d2ad0a3850704facb","cross_cats_sorted":["cs.DS","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-12-23T19:00:02Z","title_canon_sha256":"81901caf4ae2291a20b73523b6fd9ba21a32998634e288efc3e8c23cf1b3ef25"},"schema_version":"1.0","source":{"id":"2412.17899","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.17899","created_at":"2026-07-05T09:53:30Z"},{"alias_kind":"arxiv_version","alias_value":"2412.17899v1","created_at":"2026-07-05T09:53:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.17899","created_at":"2026-07-05T09:53:30Z"},{"alias_kind":"pith_short_12","alias_value":"T4P2SASE6GG7","created_at":"2026-07-05T09:53:30Z"},{"alias_kind":"pith_short_16","alias_value":"T4P2SASE6GG7HFDS","created_at":"2026-07-05T09:53:30Z"},{"alias_kind":"pith_short_8","alias_value":"T4P2SASE","created_at":"2026-07-05T09:53:30Z"}],"graph_snapshots":[{"event_id":"sha256:b40d2a86305a146c7c30b31ed5ff88828c42e430873017df587e39d64e153483","target":"graph","created_at":"2026-07-05T09:53:30Z","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/2412.17899/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The Gibbs sampler, also known as the coordinate hit-and-run algorithm, is a Markov chain that is widely used to draw samples from probability distributions in arbitrary dimensions. At each iteration of the algorithm, a randomly selected coordinate is resampled from the distribution that results from conditioning on all the other coordinates. We study the behavior of the Gibbs sampler on the class of log-smooth and strongly log-concave target distributions supported on $\\mathbb{R}^n$. Assuming the initial distribution is $M$-warm with respect to the target, we show that the Gibbs sampler requir","authors_text":"Neha S. Wadia","cross_cats":["cs.DS","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-12-23T19:00:02Z","title":"A mixing time bound for Gibbs sampling from log-smooth log-concave distributions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.17899","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":"sha256:4c98c52516322d11d0b3f7d6d17e4e3627357c1d124c73b58753a7cfef7d2fbe","target":"record","created_at":"2026-07-05T09:53:30Z","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":"b45d4352ef37ead34d0f3424c4a50992414153448f9ca29d2ad0a3850704facb","cross_cats_sorted":["cs.DS","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-12-23T19:00:02Z","title_canon_sha256":"81901caf4ae2291a20b73523b6fd9ba21a32998634e288efc3e8c23cf1b3ef25"},"schema_version":"1.0","source":{"id":"2412.17899","kind":"arxiv","version":1}},"canonical_sha256":"9f1fa90244f18df39472d5fe90be2d0b252a20ef441f2d11908dbbd86fd28ef6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9f1fa90244f18df39472d5fe90be2d0b252a20ef441f2d11908dbbd86fd28ef6","first_computed_at":"2026-07-05T09:53:30.582778Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:53:30.582778Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ySWDtTB5SP+2Qmyq9fkwj1R9la5rbR4+oO8kEtjROQuwb/FHdx0ijxUa8HzpZTnhafOMJilDPO+T/Bg4iL1jBA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:53:30.583226Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.17899","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4c98c52516322d11d0b3f7d6d17e4e3627357c1d124c73b58753a7cfef7d2fbe","sha256:b40d2a86305a146c7c30b31ed5ff88828c42e430873017df587e39d64e153483"],"state_sha256":"7f72b22cb000460e801bb6ae9d7afbc3a195ef26dd8ba58cbf9ac44905bd3051"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rW6epFQ8Dlyy7npN7l5eF7ftCcT4gtHoFXMQP975FC5roPPHMCWghdTOdvx9G8R7HkM//M6SYuTlkRdoNs80DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-02T15:32:38.779068Z","bundle_sha256":"8f55940d266375d227ab4002863e31c5b88530dbb16d8d5bceeeadb1556bda4b"}}