{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:M4NSG6ZIWXVX7WWFRNH442FQSH","short_pith_number":"pith:M4NSG6ZI","canonical_record":{"source":{"id":"1410.7692","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-10-28T16:29:29Z","cross_cats_sorted":[],"title_canon_sha256":"3544ef4819e9e939f6f53590b7541d76045cd19fba137db453b2d552a647f5d1","abstract_canon_sha256":"76ffedb0206d4cd227c5bb53f38da18ae069521545c83ac46dfd90d652666d75"},"schema_version":"1.0"},"canonical_sha256":"671b237b28b5eb7fdac58b4fce68b091cf2eeef5af70670f53b26b9fcb1621cb","source":{"kind":"arxiv","id":"1410.7692","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1410.7692","created_at":"2026-05-18T02:39:09Z"},{"alias_kind":"arxiv_version","alias_value":"1410.7692v1","created_at":"2026-05-18T02:39:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1410.7692","created_at":"2026-05-18T02:39:09Z"},{"alias_kind":"pith_short_12","alias_value":"M4NSG6ZIWXVX","created_at":"2026-05-18T12:28:38Z"},{"alias_kind":"pith_short_16","alias_value":"M4NSG6ZIWXVX7WWF","created_at":"2026-05-18T12:28:38Z"},{"alias_kind":"pith_short_8","alias_value":"M4NSG6ZI","created_at":"2026-05-18T12:28:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:M4NSG6ZIWXVX7WWFRNH442FQSH","target":"record","payload":{"canonical_record":{"source":{"id":"1410.7692","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-10-28T16:29:29Z","cross_cats_sorted":[],"title_canon_sha256":"3544ef4819e9e939f6f53590b7541d76045cd19fba137db453b2d552a647f5d1","abstract_canon_sha256":"76ffedb0206d4cd227c5bb53f38da18ae069521545c83ac46dfd90d652666d75"},"schema_version":"1.0"},"canonical_sha256":"671b237b28b5eb7fdac58b4fce68b091cf2eeef5af70670f53b26b9fcb1621cb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:39:09.700106Z","signature_b64":"e2vHN0orU9e5w25DUz7h75wiOy71UieGDrXD/JWY6E+xy8H/DcnDHqCYnAG9o4fh/Q8sdIYIFuUDU6MGQOV+Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"671b237b28b5eb7fdac58b4fce68b091cf2eeef5af70670f53b26b9fcb1621cb","last_reissued_at":"2026-05-18T02:39:09.699423Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:39:09.699423Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1410.7692","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":"2026-05-18T02:39:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9JxjiXfjVurgd2TbdC57mW2//9VL2Isbs81BoZp6+Vh5mxOmLi2yEcaq4lHAKIAtNoxP58pWeb4UXqvZ332bAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T07:13:17.131379Z"},"content_sha256":"1865651e33fd4bd297defeeb80a937c22254ff58592593896c3df5bf3d87ece0","schema_version":"1.0","event_id":"sha256:1865651e33fd4bd297defeeb80a937c22254ff58592593896c3df5bf3d87ece0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:M4NSG6ZIWXVX7WWFRNH442FQSH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scalable multiscale density estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Antonio Canale, David Dunson, Ye Wang","submitted_at":"2014-10-28T16:29:29Z","abstract_excerpt":"Although Bayesian density estimation using discrete mixtures has good performance in modest dimensions, there is a lack of statistical and computational scalability to high-dimensional multivariate cases. To combat the curse of dimensionality, it is necessary to assume the data are concentrated near a lower-dimensional subspace. However, Bayesian methods for learning this subspace along with the density of the data scale poorly computationally. To solve this problem, we propose an empirical Bayes approach, which estimates a multiscale dictionary using geometric multiresolution analysis in a fi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1410.7692","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":"2026-05-18T02:39:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vDirGoSOdIq2D9Hwivk+4IECE54jseiTH9JUoNu3xava3DYUOFZlywznMEVWOEIOqzQo1ZsL71GIQ/ddZYFjAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T07:13:17.131716Z"},"content_sha256":"207c483ddec1c2634d5596662619393396437c5aa3ab544c0905aa3451d53612","schema_version":"1.0","event_id":"sha256:207c483ddec1c2634d5596662619393396437c5aa3ab544c0905aa3451d53612"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/M4NSG6ZIWXVX7WWFRNH442FQSH/bundle.json","state_url":"https://pith.science/pith/M4NSG6ZIWXVX7WWFRNH442FQSH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/M4NSG6ZIWXVX7WWFRNH442FQSH/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":"2026-06-02T07:13:17Z","links":{"resolver":"https://pith.science/pith/M4NSG6ZIWXVX7WWFRNH442FQSH","bundle":"https://pith.science/pith/M4NSG6ZIWXVX7WWFRNH442FQSH/bundle.json","state":"https://pith.science/pith/M4NSG6ZIWXVX7WWFRNH442FQSH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/M4NSG6ZIWXVX7WWFRNH442FQSH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:M4NSG6ZIWXVX7WWFRNH442FQSH","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":"76ffedb0206d4cd227c5bb53f38da18ae069521545c83ac46dfd90d652666d75","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-10-28T16:29:29Z","title_canon_sha256":"3544ef4819e9e939f6f53590b7541d76045cd19fba137db453b2d552a647f5d1"},"schema_version":"1.0","source":{"id":"1410.7692","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1410.7692","created_at":"2026-05-18T02:39:09Z"},{"alias_kind":"arxiv_version","alias_value":"1410.7692v1","created_at":"2026-05-18T02:39:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1410.7692","created_at":"2026-05-18T02:39:09Z"},{"alias_kind":"pith_short_12","alias_value":"M4NSG6ZIWXVX","created_at":"2026-05-18T12:28:38Z"},{"alias_kind":"pith_short_16","alias_value":"M4NSG6ZIWXVX7WWF","created_at":"2026-05-18T12:28:38Z"},{"alias_kind":"pith_short_8","alias_value":"M4NSG6ZI","created_at":"2026-05-18T12:28:38Z"}],"graph_snapshots":[{"event_id":"sha256:207c483ddec1c2634d5596662619393396437c5aa3ab544c0905aa3451d53612","target":"graph","created_at":"2026-05-18T02:39:09Z","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":"Although Bayesian density estimation using discrete mixtures has good performance in modest dimensions, there is a lack of statistical and computational scalability to high-dimensional multivariate cases. To combat the curse of dimensionality, it is necessary to assume the data are concentrated near a lower-dimensional subspace. However, Bayesian methods for learning this subspace along with the density of the data scale poorly computationally. To solve this problem, we propose an empirical Bayes approach, which estimates a multiscale dictionary using geometric multiresolution analysis in a fi","authors_text":"Antonio Canale, David Dunson, Ye Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-10-28T16:29:29Z","title":"Scalable multiscale density estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1410.7692","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":"sha256:1865651e33fd4bd297defeeb80a937c22254ff58592593896c3df5bf3d87ece0","target":"record","created_at":"2026-05-18T02:39:09Z","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":"76ffedb0206d4cd227c5bb53f38da18ae069521545c83ac46dfd90d652666d75","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-10-28T16:29:29Z","title_canon_sha256":"3544ef4819e9e939f6f53590b7541d76045cd19fba137db453b2d552a647f5d1"},"schema_version":"1.0","source":{"id":"1410.7692","kind":"arxiv","version":1}},"canonical_sha256":"671b237b28b5eb7fdac58b4fce68b091cf2eeef5af70670f53b26b9fcb1621cb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"671b237b28b5eb7fdac58b4fce68b091cf2eeef5af70670f53b26b9fcb1621cb","first_computed_at":"2026-05-18T02:39:09.699423Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:39:09.699423Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"e2vHN0orU9e5w25DUz7h75wiOy71UieGDrXD/JWY6E+xy8H/DcnDHqCYnAG9o4fh/Q8sdIYIFuUDU6MGQOV+Bw==","signature_status":"signed_v1","signed_at":"2026-05-18T02:39:09.700106Z","signed_message":"canonical_sha256_bytes"},"source_id":"1410.7692","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1865651e33fd4bd297defeeb80a937c22254ff58592593896c3df5bf3d87ece0","sha256:207c483ddec1c2634d5596662619393396437c5aa3ab544c0905aa3451d53612"],"state_sha256":"da4c283c76298a81e79ff4495f79bc055092e09b0765175d95f7768ce909771d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VD+egAVuiuEnQtv1lI1aP38OFidOvhWXgDt0Y913iZ7iJjaqTwp+Wibr+E1y0XQzFdTu/Oq2MwWt9KR4+nubAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T07:13:17.133546Z","bundle_sha256":"23c725945be43a5fc8bac1b10635fe0d166c077fedc6424dc5aca107ea4e9f79"}}