{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:TCXTHSF2SYWSXRYUNPNZB4E5AC","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":"4d5f63f0afdff5b61496c4fb1da6768439d1e5817e649d2ba26f72e6117ed7e0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-01-17T10:13:49Z","title_canon_sha256":"2bc17e5751b29590eff16499d5677b6edbf64af1d522ceb46ce650d07493fbb7"},"schema_version":"1.0","source":{"id":"1701.04605","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.04605","created_at":"2026-05-18T00:19:59Z"},{"alias_kind":"arxiv_version","alias_value":"1701.04605v4","created_at":"2026-05-18T00:19:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.04605","created_at":"2026-05-18T00:19:59Z"},{"alias_kind":"pith_short_12","alias_value":"TCXTHSF2SYWS","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"TCXTHSF2SYWSXRYU","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"TCXTHSF2","created_at":"2026-05-18T12:31:46Z"}],"graph_snapshots":[{"event_id":"sha256:4b04ff55f531eb54fdac2d1df85845317cf744e3361a4b5556a942a654de161b","target":"graph","created_at":"2026-05-18T00:19:59Z","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":"Recent advances on overfitting Bayesian mixture models provide a solid and straightforward approach for inferring the underlying number of clusters and model parameters in heterogeneous datasets. The applicability of such a framework in clustering correlated high dimensional data is demonstrated. For this purpose an overfitting mixture of factor analyzers is introduced, assuming that the number of factors is fixed. A Markov chain Monte Carlo (MCMC) sampler combined with a prior parallel tempering scheme is used to estimate the posterior distribution of model parameters. The optimal number of f","authors_text":"Panagiotis Papastamoulis","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-01-17T10:13:49Z","title":"Overfitting Bayesian Mixtures of Factor Analyzers with an Unknown Number of Components"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.04605","kind":"arxiv","version":4},"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":"sha256:15156097a3c2d04eed20e7b2af2706d5847cc441615960aac227ebe803d28513","target":"record","created_at":"2026-05-18T00:19:59Z","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":"4d5f63f0afdff5b61496c4fb1da6768439d1e5817e649d2ba26f72e6117ed7e0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-01-17T10:13:49Z","title_canon_sha256":"2bc17e5751b29590eff16499d5677b6edbf64af1d522ceb46ce650d07493fbb7"},"schema_version":"1.0","source":{"id":"1701.04605","kind":"arxiv","version":4}},"canonical_sha256":"98af33c8ba962d2bc7146bdb90f09d00932cbb33bdc83e4db76dfecb466ac6c0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"98af33c8ba962d2bc7146bdb90f09d00932cbb33bdc83e4db76dfecb466ac6c0","first_computed_at":"2026-05-18T00:19:59.250680Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:19:59.250680Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pXtedfsCfWNYzaZY8hXCRDirnQeUwTXYuGT1cm0evL3d9DkW+RE2eIukPlmtRCYjJgwJ9u+CRXzrv31ZPiN+AQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:19:59.251131Z","signed_message":"canonical_sha256_bytes"},"source_id":"1701.04605","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:15156097a3c2d04eed20e7b2af2706d5847cc441615960aac227ebe803d28513","sha256:4b04ff55f531eb54fdac2d1df85845317cf744e3361a4b5556a942a654de161b"],"state_sha256":"97b45cbc7444bb25f32e3134dc67329ffbf38a83780ef58bb0ec0007cccad5c2"}