{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:OIPN2MCABN67A45XLTTBWCVWA2","short_pith_number":"pith:OIPN2MCA","canonical_record":{"source":{"id":"1410.4329","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2014-10-16T08:26:22Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"c194dac622b4ee535af4d6c3e3de9c9e31d19353dab55667b218e48e3d506d45","abstract_canon_sha256":"b2be07a7ff2af0a153ba1ba2a59d1d2a02b1b07e22a474b3c118e8f8873dc040"},"schema_version":"1.0"},"canonical_sha256":"721edd30400b7df073b75ce61b0ab6069eb939e721952cf97799caf3f9c5ddc4","source":{"kind":"arxiv","id":"1410.4329","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1410.4329","created_at":"2026-05-18T02:39:56Z"},{"alias_kind":"arxiv_version","alias_value":"1410.4329v1","created_at":"2026-05-18T02:39:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1410.4329","created_at":"2026-05-18T02:39:56Z"},{"alias_kind":"pith_short_12","alias_value":"OIPN2MCABN67","created_at":"2026-05-18T12:28:41Z"},{"alias_kind":"pith_short_16","alias_value":"OIPN2MCABN67A45X","created_at":"2026-05-18T12:28:41Z"},{"alias_kind":"pith_short_8","alias_value":"OIPN2MCA","created_at":"2026-05-18T12:28:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:OIPN2MCABN67A45XLTTBWCVWA2","target":"record","payload":{"canonical_record":{"source":{"id":"1410.4329","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2014-10-16T08:26:22Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"c194dac622b4ee535af4d6c3e3de9c9e31d19353dab55667b218e48e3d506d45","abstract_canon_sha256":"b2be07a7ff2af0a153ba1ba2a59d1d2a02b1b07e22a474b3c118e8f8873dc040"},"schema_version":"1.0"},"canonical_sha256":"721edd30400b7df073b75ce61b0ab6069eb939e721952cf97799caf3f9c5ddc4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:39:56.405771Z","signature_b64":"bizPP/jndGMkdLUeTzbjcqevoF3wyGaaXil17gy0VH7jqXGym2lfb0JR0SnbHhKhW7rn736FNZ8Ji3j12mMADQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"721edd30400b7df073b75ce61b0ab6069eb939e721952cf97799caf3f9c5ddc4","last_reissued_at":"2026-05-18T02:39:56.405308Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:39:56.405308Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1410.4329","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":"2026-05-18T02:39:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F5h371Hg5k3yb2AO4lot/ppVSl3zSBDo3+lt+DF45NmU4tDa0AzIYVKIPPZ56J8rCORrFYwZSW3KG9Zm6qeGAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T21:28:18.407996Z"},"content_sha256":"9799426646fcd8d2a7e7f1356e56345ada39621d5471c70a911c84fa61bc06be","schema_version":"1.0","event_id":"sha256:9799426646fcd8d2a7e7f1356e56345ada39621d5471c70a911c84fa61bc06be"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:OIPN2MCABN67A45XLTTBWCVWA2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Convergence rate and concentration inequalities for Gibbs sampling in high dimension","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Liming Wu, Neng-Yi Wang","submitted_at":"2014-10-16T08:26:22Z","abstract_excerpt":"The objective of this paper is to study the Gibbs sampling for computing the mean of observable in very high dimension - a powerful Markov chain Monte Carlo method. Under the Dobrushin's uniqueness condition, we establish some explicit and sharp estimate of the exponential convergence rate and prove some Gaussian concentration inequalities for the empirical mean."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1410.4329","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":""},"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":"2026-05-18T02:39:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3D4aABIcuRvARROiWh5zel2yNIhJqfoa/oFryFYMoR6waPCXQIC39u1/UpSpSaBHJQWYaIpN1UdYd0jm+i7DAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T21:28:18.408635Z"},"content_sha256":"c72820fb0b3622c7891906ad11389ad7f0ab43756877b9de449f76132633e660","schema_version":"1.0","event_id":"sha256:c72820fb0b3622c7891906ad11389ad7f0ab43756877b9de449f76132633e660"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OIPN2MCABN67A45XLTTBWCVWA2/bundle.json","state_url":"https://pith.science/pith/OIPN2MCABN67A45XLTTBWCVWA2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OIPN2MCABN67A45XLTTBWCVWA2/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":"2026-06-05T21:28:18Z","links":{"resolver":"https://pith.science/pith/OIPN2MCABN67A45XLTTBWCVWA2","bundle":"https://pith.science/pith/OIPN2MCABN67A45XLTTBWCVWA2/bundle.json","state":"https://pith.science/pith/OIPN2MCABN67A45XLTTBWCVWA2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OIPN2MCABN67A45XLTTBWCVWA2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:OIPN2MCABN67A45XLTTBWCVWA2","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":"b2be07a7ff2af0a153ba1ba2a59d1d2a02b1b07e22a474b3c118e8f8873dc040","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2014-10-16T08:26:22Z","title_canon_sha256":"c194dac622b4ee535af4d6c3e3de9c9e31d19353dab55667b218e48e3d506d45"},"schema_version":"1.0","source":{"id":"1410.4329","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1410.4329","created_at":"2026-05-18T02:39:56Z"},{"alias_kind":"arxiv_version","alias_value":"1410.4329v1","created_at":"2026-05-18T02:39:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1410.4329","created_at":"2026-05-18T02:39:56Z"},{"alias_kind":"pith_short_12","alias_value":"OIPN2MCABN67","created_at":"2026-05-18T12:28:41Z"},{"alias_kind":"pith_short_16","alias_value":"OIPN2MCABN67A45X","created_at":"2026-05-18T12:28:41Z"},{"alias_kind":"pith_short_8","alias_value":"OIPN2MCA","created_at":"2026-05-18T12:28:41Z"}],"graph_snapshots":[{"event_id":"sha256:c72820fb0b3622c7891906ad11389ad7f0ab43756877b9de449f76132633e660","target":"graph","created_at":"2026-05-18T02:39:56Z","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"},"paper":{"abstract_excerpt":"The objective of this paper is to study the Gibbs sampling for computing the mean of observable in very high dimension - a powerful Markov chain Monte Carlo method. Under the Dobrushin's uniqueness condition, we establish some explicit and sharp estimate of the exponential convergence rate and prove some Gaussian concentration inequalities for the empirical mean.","authors_text":"Liming Wu, Neng-Yi Wang","cross_cats":["stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2014-10-16T08:26:22Z","title":"Convergence rate and concentration inequalities for Gibbs sampling in high dimension"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1410.4329","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":"sha256:9799426646fcd8d2a7e7f1356e56345ada39621d5471c70a911c84fa61bc06be","target":"record","created_at":"2026-05-18T02:39:56Z","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":"b2be07a7ff2af0a153ba1ba2a59d1d2a02b1b07e22a474b3c118e8f8873dc040","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2014-10-16T08:26:22Z","title_canon_sha256":"c194dac622b4ee535af4d6c3e3de9c9e31d19353dab55667b218e48e3d506d45"},"schema_version":"1.0","source":{"id":"1410.4329","kind":"arxiv","version":1}},"canonical_sha256":"721edd30400b7df073b75ce61b0ab6069eb939e721952cf97799caf3f9c5ddc4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"721edd30400b7df073b75ce61b0ab6069eb939e721952cf97799caf3f9c5ddc4","first_computed_at":"2026-05-18T02:39:56.405308Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:39:56.405308Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bizPP/jndGMkdLUeTzbjcqevoF3wyGaaXil17gy0VH7jqXGym2lfb0JR0SnbHhKhW7rn736FNZ8Ji3j12mMADQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:39:56.405771Z","signed_message":"canonical_sha256_bytes"},"source_id":"1410.4329","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9799426646fcd8d2a7e7f1356e56345ada39621d5471c70a911c84fa61bc06be","sha256:c72820fb0b3622c7891906ad11389ad7f0ab43756877b9de449f76132633e660"],"state_sha256":"1621771a1bc66ecb2bcf8acc3c514b39c6040704a898da9cff80fdbeba40d703"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1kFY7eOVHLPSnrF1cnBofveo1dfOvmO1JaTBRAcXXZeh8UYUl26W5fAUKpZRJ120CbS3b1cxKkPmxOg4/ZuyBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T21:28:18.412151Z","bundle_sha256":"20be1141de9a6627eec0ebaf24c24d9fe7ad0ca5ad934fc904eb9c90b2922d18"}}