{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:4TK53HOWGDD5BWNCDTEJFLKAYR","short_pith_number":"pith:4TK53HOW","canonical_record":{"source":{"id":"1210.2601","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2012-10-09T14:05:38Z","cross_cats_sorted":["math.PR","math.ST","stat.ME","stat.TH"],"title_canon_sha256":"1d2f6d1db4a2f0666eb3840abbfd3e52cf46d40c3a611eebc9541f6a4fa8c9bb","abstract_canon_sha256":"f67e902a7775faf97863da6d8bc60574499cdfd58a63d34124811c35e4b31848"},"schema_version":"1.0"},"canonical_sha256":"e4d5dd9dd630c7d0d9a21cc892ad40c464d34d3e4b58d755da35ada6b0f92a14","source":{"kind":"arxiv","id":"1210.2601","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1210.2601","created_at":"2026-05-18T01:09:26Z"},{"alias_kind":"arxiv_version","alias_value":"1210.2601v3","created_at":"2026-05-18T01:09:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1210.2601","created_at":"2026-05-18T01:09:26Z"},{"alias_kind":"pith_short_12","alias_value":"4TK53HOWGDD5","created_at":"2026-05-18T12:26:53Z"},{"alias_kind":"pith_short_16","alias_value":"4TK53HOWGDD5BWNC","created_at":"2026-05-18T12:26:53Z"},{"alias_kind":"pith_short_8","alias_value":"4TK53HOW","created_at":"2026-05-18T12:26:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:4TK53HOWGDD5BWNCDTEJFLKAYR","target":"record","payload":{"canonical_record":{"source":{"id":"1210.2601","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2012-10-09T14:05:38Z","cross_cats_sorted":["math.PR","math.ST","stat.ME","stat.TH"],"title_canon_sha256":"1d2f6d1db4a2f0666eb3840abbfd3e52cf46d40c3a611eebc9541f6a4fa8c9bb","abstract_canon_sha256":"f67e902a7775faf97863da6d8bc60574499cdfd58a63d34124811c35e4b31848"},"schema_version":"1.0"},"canonical_sha256":"e4d5dd9dd630c7d0d9a21cc892ad40c464d34d3e4b58d755da35ada6b0f92a14","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:09:26.273613Z","signature_b64":"Zh6r9mFPwVGh2E0vf34+H4ItkE4j8IAbJGfWCRORgDLuzGEfl7oXKLa0jxi7W8d/W6M4FczUJ6pMbq26d2GPDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e4d5dd9dd630c7d0d9a21cc892ad40c464d34d3e4b58d755da35ada6b0f92a14","last_reissued_at":"2026-05-18T01:09:26.273154Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:09:26.273154Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1210.2601","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:09:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Kdb8l2r/REb9uXrI/XbDEur9iUsVcRlU3ltJX06YKs6nKlSxweynHC8g2EdpYDGrNoxbZjIYlp+YacWPurFtBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T00:36:21.113407Z"},"content_sha256":"b4dc94d357dcad4a7f8a51546f1b0bd7cfb0f6ef89b7166e9e63658ee282093c","schema_version":"1.0","event_id":"sha256:b4dc94d357dcad4a7f8a51546f1b0bd7cfb0f6ef89b7166e9e63658ee282093c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:4TK53HOWGDD5BWNCDTEJFLKAYR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adaptive MCMC with online relabeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR","math.ST","stat.ME","stat.TH"],"primary_cat":"stat.CO","authors_text":"Bal\\'azs K\\'egl, Gersende Fort, Olivier Capp\\'e, R\\'emi Bardenet","submitted_at":"2012-10-09T14:05:38Z","abstract_excerpt":"When targeting a distribution that is artificially invariant under some permutations, Markov chain Monte Carlo (MCMC) algorithms face the label-switching problem, rendering marginal inference particularly cumbersome. Such a situation arises, for example, in the Bayesian analysis of finite mixture models. Adaptive MCMC algorithms such as adaptive Metropolis (AM), which self-calibrates its proposal distribution using an online estimate of the covariance matrix of the target, are no exception. To address the label-switching issue, relabeling algorithms associate a permutation to each MCMC sample,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1210.2601","kind":"arxiv","version":3},"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":"2026-05-18T01:09:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QYzhwBe9Tm8vMQMhznjxxch7AkoTmABqRpAdbJEKEN9qcVWB09hmegWU4qsBajiW5Bf7pZOfyAWnlgkhrTPxBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T00:36:21.114080Z"},"content_sha256":"9630718e60ada6a9d39773f7a512e0a5ef7fb09f75d393d26206bd15bad746fe","schema_version":"1.0","event_id":"sha256:9630718e60ada6a9d39773f7a512e0a5ef7fb09f75d393d26206bd15bad746fe"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4TK53HOWGDD5BWNCDTEJFLKAYR/bundle.json","state_url":"https://pith.science/pith/4TK53HOWGDD5BWNCDTEJFLKAYR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4TK53HOWGDD5BWNCDTEJFLKAYR/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":"2026-05-22T00:36:21Z","links":{"resolver":"https://pith.science/pith/4TK53HOWGDD5BWNCDTEJFLKAYR","bundle":"https://pith.science/pith/4TK53HOWGDD5BWNCDTEJFLKAYR/bundle.json","state":"https://pith.science/pith/4TK53HOWGDD5BWNCDTEJFLKAYR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4TK53HOWGDD5BWNCDTEJFLKAYR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:4TK53HOWGDD5BWNCDTEJFLKAYR","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":"f67e902a7775faf97863da6d8bc60574499cdfd58a63d34124811c35e4b31848","cross_cats_sorted":["math.PR","math.ST","stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2012-10-09T14:05:38Z","title_canon_sha256":"1d2f6d1db4a2f0666eb3840abbfd3e52cf46d40c3a611eebc9541f6a4fa8c9bb"},"schema_version":"1.0","source":{"id":"1210.2601","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1210.2601","created_at":"2026-05-18T01:09:26Z"},{"alias_kind":"arxiv_version","alias_value":"1210.2601v3","created_at":"2026-05-18T01:09:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1210.2601","created_at":"2026-05-18T01:09:26Z"},{"alias_kind":"pith_short_12","alias_value":"4TK53HOWGDD5","created_at":"2026-05-18T12:26:53Z"},{"alias_kind":"pith_short_16","alias_value":"4TK53HOWGDD5BWNC","created_at":"2026-05-18T12:26:53Z"},{"alias_kind":"pith_short_8","alias_value":"4TK53HOW","created_at":"2026-05-18T12:26:53Z"}],"graph_snapshots":[{"event_id":"sha256:9630718e60ada6a9d39773f7a512e0a5ef7fb09f75d393d26206bd15bad746fe","target":"graph","created_at":"2026-05-18T01:09:26Z","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":"When targeting a distribution that is artificially invariant under some permutations, Markov chain Monte Carlo (MCMC) algorithms face the label-switching problem, rendering marginal inference particularly cumbersome. Such a situation arises, for example, in the Bayesian analysis of finite mixture models. Adaptive MCMC algorithms such as adaptive Metropolis (AM), which self-calibrates its proposal distribution using an online estimate of the covariance matrix of the target, are no exception. To address the label-switching issue, relabeling algorithms associate a permutation to each MCMC sample,","authors_text":"Bal\\'azs K\\'egl, Gersende Fort, Olivier Capp\\'e, R\\'emi Bardenet","cross_cats":["math.PR","math.ST","stat.ME","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2012-10-09T14:05:38Z","title":"Adaptive MCMC with online relabeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1210.2601","kind":"arxiv","version":3},"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:b4dc94d357dcad4a7f8a51546f1b0bd7cfb0f6ef89b7166e9e63658ee282093c","target":"record","created_at":"2026-05-18T01:09:26Z","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":"f67e902a7775faf97863da6d8bc60574499cdfd58a63d34124811c35e4b31848","cross_cats_sorted":["math.PR","math.ST","stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2012-10-09T14:05:38Z","title_canon_sha256":"1d2f6d1db4a2f0666eb3840abbfd3e52cf46d40c3a611eebc9541f6a4fa8c9bb"},"schema_version":"1.0","source":{"id":"1210.2601","kind":"arxiv","version":3}},"canonical_sha256":"e4d5dd9dd630c7d0d9a21cc892ad40c464d34d3e4b58d755da35ada6b0f92a14","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e4d5dd9dd630c7d0d9a21cc892ad40c464d34d3e4b58d755da35ada6b0f92a14","first_computed_at":"2026-05-18T01:09:26.273154Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:09:26.273154Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Zh6r9mFPwVGh2E0vf34+H4ItkE4j8IAbJGfWCRORgDLuzGEfl7oXKLa0jxi7W8d/W6M4FczUJ6pMbq26d2GPDw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:09:26.273613Z","signed_message":"canonical_sha256_bytes"},"source_id":"1210.2601","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b4dc94d357dcad4a7f8a51546f1b0bd7cfb0f6ef89b7166e9e63658ee282093c","sha256:9630718e60ada6a9d39773f7a512e0a5ef7fb09f75d393d26206bd15bad746fe"],"state_sha256":"8ff0b923240048e4387d906cfcf0f2738f936d66df98f93af6ba8932125241f6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N2WZgsUyUoCa+LFC12NpqlWgZbPX953wS1wszfj96op/nI64DLH4mjpIAiXUeHNt4R8MXDBGM6LD7q9OGcZMBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-22T00:36:21.117120Z","bundle_sha256":"524f43e18e5529ebcecc008d7d70a3940b66f74d759c856a6486f28b0dc10e87"}}