{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:RHUHFVLXPVLUQUFWPOUAMDVDP2","short_pith_number":"pith:RHUHFVLX","canonical_record":{"source":{"id":"1703.02834","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-03-08T13:47:17Z","cross_cats_sorted":["math.ST","stat.ML","stat.TH"],"title_canon_sha256":"26d30c7277fbe001a67db363593624eabac6fd5fae8ec918cdc752ad0bf14dfc","abstract_canon_sha256":"0f8f523edc25efbe3fa5a443df903c16a632f883a07b30e63d0476d54bb5c542"},"schema_version":"1.0"},"canonical_sha256":"89e872d5777d574850b67ba8060ea37e981abb75ed68bb35a719220090d5022a","source":{"kind":"arxiv","id":"1703.02834","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.02834","created_at":"2026-05-17T23:45:47Z"},{"alias_kind":"arxiv_version","alias_value":"1703.02834v2","created_at":"2026-05-17T23:45:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.02834","created_at":"2026-05-17T23:45:47Z"},{"alias_kind":"pith_short_12","alias_value":"RHUHFVLXPVLU","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"RHUHFVLXPVLUQUFW","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"RHUHFVLX","created_at":"2026-05-18T12:31:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:RHUHFVLXPVLUQUFWPOUAMDVDP2","target":"record","payload":{"canonical_record":{"source":{"id":"1703.02834","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-03-08T13:47:17Z","cross_cats_sorted":["math.ST","stat.ML","stat.TH"],"title_canon_sha256":"26d30c7277fbe001a67db363593624eabac6fd5fae8ec918cdc752ad0bf14dfc","abstract_canon_sha256":"0f8f523edc25efbe3fa5a443df903c16a632f883a07b30e63d0476d54bb5c542"},"schema_version":"1.0"},"canonical_sha256":"89e872d5777d574850b67ba8060ea37e981abb75ed68bb35a719220090d5022a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:47.049546Z","signature_b64":"MKa5bET7MTSQa/pGGwq+EhF5WfIKZZ/uRN84/oPOi6ZSscNSG5b7akG9CG8Um9Adb0lNiGWYQOMEJ9hRGXXADg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89e872d5777d574850b67ba8060ea37e981abb75ed68bb35a719220090d5022a","last_reissued_at":"2026-05-17T23:45:47.048869Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:47.048869Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.02834","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:45:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vlVZSiz0dZ0CBycdplUszd3jaj74u9+hE+kVmfOZGj3xXcwrooC7AZPHiQQSeMxFKTFXKVAPZDRdauLQsbUtDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T21:04:26.858077Z"},"content_sha256":"a7d9ad1d4a45eac3f2aa6033d73b35b9bc2e7a59e43610517a066be9b8fffb26","schema_version":"1.0","event_id":"sha256:a7d9ad1d4a45eac3f2aa6033d73b35b9bc2e7a59e43610517a066be9b8fffb26"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:RHUHFVLXPVLUQUFWPOUAMDVDP2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Exact Dimensionality Selection for Bayesian PCA","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Charles Bouveyron (EPIONE, JAD), Pierre-Alexandre Mattei, Pierre Latouche (MAP5 - UMR 8145)","submitted_at":"2017-03-08T13:47:17Z","abstract_excerpt":"We present a Bayesian model selection approach to estimate the intrinsic dimensionality of a high-dimensional dataset. To this end, we introduce a novel formulation of the probabilisitic principal component analysis model based on a normal-gamma prior distribution. In this context, we exhibit a closed-form expression of the marginal likelihood which allows to infer an optimal number of components. We also propose a heuristic based on the expected shape of the marginal likelihood curve in order to choose the hyperparameters. In non-asymptotic frameworks, we show on simulated data that this exac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.02834","kind":"arxiv","version":2},"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":"2026-05-17T23:45:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eYsTEketLfbj+E+kg7dViItC/bG8jEBu1JqExZblguKpIyDPVgq3Kq2IPeo51+XlJo/tzS3WqzRCXEXUFjOpBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T21:04:26.858843Z"},"content_sha256":"43b1f2f1e680ce1ba4b69bf7deceb841c8f6bb42c2dfc7b35f6d957128ffbd2a","schema_version":"1.0","event_id":"sha256:43b1f2f1e680ce1ba4b69bf7deceb841c8f6bb42c2dfc7b35f6d957128ffbd2a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RHUHFVLXPVLUQUFWPOUAMDVDP2/bundle.json","state_url":"https://pith.science/pith/RHUHFVLXPVLUQUFWPOUAMDVDP2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RHUHFVLXPVLUQUFWPOUAMDVDP2/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":"2026-06-11T21:04:26Z","links":{"resolver":"https://pith.science/pith/RHUHFVLXPVLUQUFWPOUAMDVDP2","bundle":"https://pith.science/pith/RHUHFVLXPVLUQUFWPOUAMDVDP2/bundle.json","state":"https://pith.science/pith/RHUHFVLXPVLUQUFWPOUAMDVDP2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RHUHFVLXPVLUQUFWPOUAMDVDP2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:RHUHFVLXPVLUQUFWPOUAMDVDP2","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":"0f8f523edc25efbe3fa5a443df903c16a632f883a07b30e63d0476d54bb5c542","cross_cats_sorted":["math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-03-08T13:47:17Z","title_canon_sha256":"26d30c7277fbe001a67db363593624eabac6fd5fae8ec918cdc752ad0bf14dfc"},"schema_version":"1.0","source":{"id":"1703.02834","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.02834","created_at":"2026-05-17T23:45:47Z"},{"alias_kind":"arxiv_version","alias_value":"1703.02834v2","created_at":"2026-05-17T23:45:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.02834","created_at":"2026-05-17T23:45:47Z"},{"alias_kind":"pith_short_12","alias_value":"RHUHFVLXPVLU","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"RHUHFVLXPVLUQUFW","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"RHUHFVLX","created_at":"2026-05-18T12:31:39Z"}],"graph_snapshots":[{"event_id":"sha256:43b1f2f1e680ce1ba4b69bf7deceb841c8f6bb42c2dfc7b35f6d957128ffbd2a","target":"graph","created_at":"2026-05-17T23:45:47Z","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":"We present a Bayesian model selection approach to estimate the intrinsic dimensionality of a high-dimensional dataset. To this end, we introduce a novel formulation of the probabilisitic principal component analysis model based on a normal-gamma prior distribution. In this context, we exhibit a closed-form expression of the marginal likelihood which allows to infer an optimal number of components. We also propose a heuristic based on the expected shape of the marginal likelihood curve in order to choose the hyperparameters. In non-asymptotic frameworks, we show on simulated data that this exac","authors_text":"Charles Bouveyron (EPIONE, JAD), Pierre-Alexandre Mattei, Pierre Latouche (MAP5 - UMR 8145)","cross_cats":["math.ST","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-03-08T13:47:17Z","title":"Exact Dimensionality Selection for Bayesian PCA"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.02834","kind":"arxiv","version":2},"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:a7d9ad1d4a45eac3f2aa6033d73b35b9bc2e7a59e43610517a066be9b8fffb26","target":"record","created_at":"2026-05-17T23:45:47Z","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":"0f8f523edc25efbe3fa5a443df903c16a632f883a07b30e63d0476d54bb5c542","cross_cats_sorted":["math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-03-08T13:47:17Z","title_canon_sha256":"26d30c7277fbe001a67db363593624eabac6fd5fae8ec918cdc752ad0bf14dfc"},"schema_version":"1.0","source":{"id":"1703.02834","kind":"arxiv","version":2}},"canonical_sha256":"89e872d5777d574850b67ba8060ea37e981abb75ed68bb35a719220090d5022a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"89e872d5777d574850b67ba8060ea37e981abb75ed68bb35a719220090d5022a","first_computed_at":"2026-05-17T23:45:47.048869Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:45:47.048869Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MKa5bET7MTSQa/pGGwq+EhF5WfIKZZ/uRN84/oPOi6ZSscNSG5b7akG9CG8Um9Adb0lNiGWYQOMEJ9hRGXXADg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:45:47.049546Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.02834","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a7d9ad1d4a45eac3f2aa6033d73b35b9bc2e7a59e43610517a066be9b8fffb26","sha256:43b1f2f1e680ce1ba4b69bf7deceb841c8f6bb42c2dfc7b35f6d957128ffbd2a"],"state_sha256":"6d4986cee906ff80399adbd916aa3cb61ebec6bbfd3655f4e55ff33d55e462a3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f3X2km3UB5R784vZy5F5JDPgRgnpSJszz5mdnCYdauNMG+Mrehn5Bey6Xz6KXh0xf10HD/KaKjj1SuiWKiv6CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T21:04:26.862763Z","bundle_sha256":"9e039319ce9a956751fc90cde26a8b43c6198516aa801990e21028d8d73a7ed8"}}