{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:4UWU77EWCWZPBWRLL7B3EWKQ5Z","short_pith_number":"pith:4UWU77EW","canonical_record":{"source":{"id":"1406.0071","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-05-31T11:50:03Z","cross_cats_sorted":[],"title_canon_sha256":"af28baf416d6e9c65b4c64e5761f8d31a73d994f5fc8db606951cd38d2745b3c","abstract_canon_sha256":"87d806e7769186837839386574cd105b1a0c37abb4281d82f4a81c85beedfa2c"},"schema_version":"1.0"},"canonical_sha256":"e52d4ffc9615b2f0da2b5fc3b25950ee4a8be1e9d19bfd32c7a8bb88d1a2dbd3","source":{"kind":"arxiv","id":"1406.0071","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1406.0071","created_at":"2026-05-18T02:50:41Z"},{"alias_kind":"arxiv_version","alias_value":"1406.0071v1","created_at":"2026-05-18T02:50:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1406.0071","created_at":"2026-05-18T02:50:41Z"},{"alias_kind":"pith_short_12","alias_value":"4UWU77EWCWZP","created_at":"2026-05-18T12:28:14Z"},{"alias_kind":"pith_short_16","alias_value":"4UWU77EWCWZPBWRL","created_at":"2026-05-18T12:28:14Z"},{"alias_kind":"pith_short_8","alias_value":"4UWU77EW","created_at":"2026-05-18T12:28:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:4UWU77EWCWZPBWRLL7B3EWKQ5Z","target":"record","payload":{"canonical_record":{"source":{"id":"1406.0071","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-05-31T11:50:03Z","cross_cats_sorted":[],"title_canon_sha256":"af28baf416d6e9c65b4c64e5761f8d31a73d994f5fc8db606951cd38d2745b3c","abstract_canon_sha256":"87d806e7769186837839386574cd105b1a0c37abb4281d82f4a81c85beedfa2c"},"schema_version":"1.0"},"canonical_sha256":"e52d4ffc9615b2f0da2b5fc3b25950ee4a8be1e9d19bfd32c7a8bb88d1a2dbd3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:50:41.862928Z","signature_b64":"ukwBIWtAXJjttMleOUCY5sl09vCaLKqD9P0I/rzPtDtWm4fr/niggOyzRM3ofGNk3jt41CsL5jmE+nLnm2eFBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e52d4ffc9615b2f0da2b5fc3b25950ee4a8be1e9d19bfd32c7a8bb88d1a2dbd3","last_reissued_at":"2026-05-18T02:50:41.862289Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:50:41.862289Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1406.0071","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":"2026-05-18T02:50:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ql5+N1dz8lUZb6WYqocvQFhc0WGQNwXJxutR6HstfDklNzJug4fxLKwUwfvuw27Bm5R7YeGNaX9RmnLY3jZhBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T23:18:40.647189Z"},"content_sha256":"509b103332fe5772ba8c73d156c4bd7786eb9391bb402b6bc875b1cabbe7465b","schema_version":"1.0","event_id":"sha256:509b103332fe5772ba8c73d156c4bd7786eb9391bb402b6bc875b1cabbe7465b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:4UWU77EWCWZPBWRLL7B3EWKQ5Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adaptive Reconfiguration Moves for Dirichlet Mixtures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Mikkel N. Schmidt, Morten M{\\o}rup, Tue Herlau, Yee Whye Teh","submitted_at":"2014-05-31T11:50:03Z","abstract_excerpt":"Bayesian mixture models are widely applied for unsupervised learning and exploratory data analysis. Markov chain Monte Carlo based on Gibbs sampling and split-merge moves are widely used for inference in these models. However, both methods are restricted to limited types of transitions and suffer from torpid mixing and low accept rates even for problems of modest size. We propose a method that considers a broader range of transitions that are close to equilibrium by exploiting multiple chains in parallel and using the past states adaptively to inform the proposal distribution. The method signi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1406.0071","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":"2026-05-18T02:50:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pIfOtV3zIWr1hC+NP/3kKU/FuZhWZ360edM1X+81iNl5rSFF1BrtEujJmN2cGxxhWG7CTi+43tJbwo8cw99+Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T23:18:40.647733Z"},"content_sha256":"335a4df9d169a149d46b43682aa3342be689df5c3d1c48f8c3df5f3d55e7b103","schema_version":"1.0","event_id":"sha256:335a4df9d169a149d46b43682aa3342be689df5c3d1c48f8c3df5f3d55e7b103"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4UWU77EWCWZPBWRLL7B3EWKQ5Z/bundle.json","state_url":"https://pith.science/pith/4UWU77EWCWZPBWRLL7B3EWKQ5Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4UWU77EWCWZPBWRLL7B3EWKQ5Z/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":"2026-05-24T23:18:40Z","links":{"resolver":"https://pith.science/pith/4UWU77EWCWZPBWRLL7B3EWKQ5Z","bundle":"https://pith.science/pith/4UWU77EWCWZPBWRLL7B3EWKQ5Z/bundle.json","state":"https://pith.science/pith/4UWU77EWCWZPBWRLL7B3EWKQ5Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4UWU77EWCWZPBWRLL7B3EWKQ5Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:4UWU77EWCWZPBWRLL7B3EWKQ5Z","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":"87d806e7769186837839386574cd105b1a0c37abb4281d82f4a81c85beedfa2c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-05-31T11:50:03Z","title_canon_sha256":"af28baf416d6e9c65b4c64e5761f8d31a73d994f5fc8db606951cd38d2745b3c"},"schema_version":"1.0","source":{"id":"1406.0071","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1406.0071","created_at":"2026-05-18T02:50:41Z"},{"alias_kind":"arxiv_version","alias_value":"1406.0071v1","created_at":"2026-05-18T02:50:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1406.0071","created_at":"2026-05-18T02:50:41Z"},{"alias_kind":"pith_short_12","alias_value":"4UWU77EWCWZP","created_at":"2026-05-18T12:28:14Z"},{"alias_kind":"pith_short_16","alias_value":"4UWU77EWCWZPBWRL","created_at":"2026-05-18T12:28:14Z"},{"alias_kind":"pith_short_8","alias_value":"4UWU77EW","created_at":"2026-05-18T12:28:14Z"}],"graph_snapshots":[{"event_id":"sha256:335a4df9d169a149d46b43682aa3342be689df5c3d1c48f8c3df5f3d55e7b103","target":"graph","created_at":"2026-05-18T02:50:41Z","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":"Bayesian mixture models are widely applied for unsupervised learning and exploratory data analysis. Markov chain Monte Carlo based on Gibbs sampling and split-merge moves are widely used for inference in these models. However, both methods are restricted to limited types of transitions and suffer from torpid mixing and low accept rates even for problems of modest size. We propose a method that considers a broader range of transitions that are close to equilibrium by exploiting multiple chains in parallel and using the past states adaptively to inform the proposal distribution. The method signi","authors_text":"Mikkel N. Schmidt, Morten M{\\o}rup, Tue Herlau, Yee Whye Teh","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-05-31T11:50:03Z","title":"Adaptive Reconfiguration Moves for Dirichlet Mixtures"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1406.0071","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":"sha256:509b103332fe5772ba8c73d156c4bd7786eb9391bb402b6bc875b1cabbe7465b","target":"record","created_at":"2026-05-18T02:50:41Z","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":"87d806e7769186837839386574cd105b1a0c37abb4281d82f4a81c85beedfa2c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-05-31T11:50:03Z","title_canon_sha256":"af28baf416d6e9c65b4c64e5761f8d31a73d994f5fc8db606951cd38d2745b3c"},"schema_version":"1.0","source":{"id":"1406.0071","kind":"arxiv","version":1}},"canonical_sha256":"e52d4ffc9615b2f0da2b5fc3b25950ee4a8be1e9d19bfd32c7a8bb88d1a2dbd3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e52d4ffc9615b2f0da2b5fc3b25950ee4a8be1e9d19bfd32c7a8bb88d1a2dbd3","first_computed_at":"2026-05-18T02:50:41.862289Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:50:41.862289Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ukwBIWtAXJjttMleOUCY5sl09vCaLKqD9P0I/rzPtDtWm4fr/niggOyzRM3ofGNk3jt41CsL5jmE+nLnm2eFBA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:50:41.862928Z","signed_message":"canonical_sha256_bytes"},"source_id":"1406.0071","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:509b103332fe5772ba8c73d156c4bd7786eb9391bb402b6bc875b1cabbe7465b","sha256:335a4df9d169a149d46b43682aa3342be689df5c3d1c48f8c3df5f3d55e7b103"],"state_sha256":"d102463c75b98ffe0383d1205692316fcb1bc3883afe7bd0dec3929877876118"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QjnBTsuhAiTEfht38gIMOz3+MSHwZD6DIhdyCDzv8kTwk78/WG55rVky8g1FEx1x2sO9T3eLUJkZ54+JNAV2AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-24T23:18:40.650240Z","bundle_sha256":"60bb309218c702e329a47cafbf22bcafa0219d83ba1940ed3afcf5da1d867c2d"}}