{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:33WB6VZEONNNZH757PBZ33QY6A","short_pith_number":"pith:33WB6VZE","canonical_record":{"source":{"id":"1604.07451","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2016-04-25T21:20:51Z","cross_cats_sorted":["stat.CO","stat.ME","stat.ML","stat.TH"],"title_canon_sha256":"3a1815269c90f0deaa7864e0b016162d9e5491e9748f321166798a2ffc00ea25","abstract_canon_sha256":"b918c59a94649d432630411a99dbdefd449df36390658cedced5f8c9e437e3f1"},"schema_version":"1.0"},"canonical_sha256":"deec1f5724735adc9ffdfbc39dee18f020e3d51082e01603955a873d39163e49","source":{"kind":"arxiv","id":"1604.07451","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1604.07451","created_at":"2026-05-18T00:28:32Z"},{"alias_kind":"arxiv_version","alias_value":"1604.07451v3","created_at":"2026-05-18T00:28:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.07451","created_at":"2026-05-18T00:28:32Z"},{"alias_kind":"pith_short_12","alias_value":"33WB6VZEONNN","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_16","alias_value":"33WB6VZEONNNZH75","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_8","alias_value":"33WB6VZE","created_at":"2026-05-18T12:29:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:33WB6VZEONNNZH757PBZ33QY6A","target":"record","payload":{"canonical_record":{"source":{"id":"1604.07451","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2016-04-25T21:20:51Z","cross_cats_sorted":["stat.CO","stat.ME","stat.ML","stat.TH"],"title_canon_sha256":"3a1815269c90f0deaa7864e0b016162d9e5491e9748f321166798a2ffc00ea25","abstract_canon_sha256":"b918c59a94649d432630411a99dbdefd449df36390658cedced5f8c9e437e3f1"},"schema_version":"1.0"},"canonical_sha256":"deec1f5724735adc9ffdfbc39dee18f020e3d51082e01603955a873d39163e49","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:28:32.589496Z","signature_b64":"RlCKx1mAYdWjIsFjPKpPCD54J7Rtn6pj87fM8oYDKbEOdbtclqK/jW/qy1FXNX+z8m7ppb7kcdJq3c9/mMraAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"deec1f5724735adc9ffdfbc39dee18f020e3d51082e01603955a873d39163e49","last_reissued_at":"2026-05-18T00:28:32.588544Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:28:32.588544Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1604.07451","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":"2026-05-18T00:28:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4xfz2haANx/dx8YGE8XoUZd49KiSgszLZYsfNXXuZnPkIrqEzXLsAjn/o5YOwOT+YDraQTsRt/Q5OW/NGX0oCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T22:11:54.064305Z"},"content_sha256":"fb8da59a7e61dd490a61122b53a0a2e7a1fdf212c62e90c5a04f1ac1802a03bf","schema_version":"1.0","event_id":"sha256:fb8da59a7e61dd490a61122b53a0a2e7a1fdf212c62e90c5a04f1ac1802a03bf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:33WB6VZEONNNZH757PBZ33QY6A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Local Dependence In Ordered Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO","stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Guo Yu, Jacob Bien","submitted_at":"2016-04-25T21:20:51Z","abstract_excerpt":"In many applications, data come with a natural ordering. This ordering can often induce local dependence among nearby variables. However, in complex data, the width of this dependence may vary, making simple assumptions such as a constant neighborhood size unrealistic. We propose a framework for learning this local dependence based on estimating the inverse of the Cholesky factor of the covariance matrix. Penalized maximum likelihood estimation of this matrix yields a simple regression interpretation for local dependence in which variables are predicted by their neighbors. Our proposed method "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.07451","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":"2026-05-18T00:28:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hsOPmGfIGtiJUJ4a+H2mRv9GKWbCQ1JOOkLD2fxCzFK6EXmXkAZ1QvZ4usjQZaAufvLwHD/WFw/bLQcVoActDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T22:11:54.064861Z"},"content_sha256":"52972f0843e3903fea51c718a00723ada7119558a40b33334750eabd2992b5ab","schema_version":"1.0","event_id":"sha256:52972f0843e3903fea51c718a00723ada7119558a40b33334750eabd2992b5ab"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/33WB6VZEONNNZH757PBZ33QY6A/bundle.json","state_url":"https://pith.science/pith/33WB6VZEONNNZH757PBZ33QY6A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/33WB6VZEONNNZH757PBZ33QY6A/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":"2026-06-05T22:11:54Z","links":{"resolver":"https://pith.science/pith/33WB6VZEONNNZH757PBZ33QY6A","bundle":"https://pith.science/pith/33WB6VZEONNNZH757PBZ33QY6A/bundle.json","state":"https://pith.science/pith/33WB6VZEONNNZH757PBZ33QY6A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/33WB6VZEONNNZH757PBZ33QY6A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:33WB6VZEONNNZH757PBZ33QY6A","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":"b918c59a94649d432630411a99dbdefd449df36390658cedced5f8c9e437e3f1","cross_cats_sorted":["stat.CO","stat.ME","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2016-04-25T21:20:51Z","title_canon_sha256":"3a1815269c90f0deaa7864e0b016162d9e5491e9748f321166798a2ffc00ea25"},"schema_version":"1.0","source":{"id":"1604.07451","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1604.07451","created_at":"2026-05-18T00:28:32Z"},{"alias_kind":"arxiv_version","alias_value":"1604.07451v3","created_at":"2026-05-18T00:28:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.07451","created_at":"2026-05-18T00:28:32Z"},{"alias_kind":"pith_short_12","alias_value":"33WB6VZEONNN","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_16","alias_value":"33WB6VZEONNNZH75","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_8","alias_value":"33WB6VZE","created_at":"2026-05-18T12:29:55Z"}],"graph_snapshots":[{"event_id":"sha256:52972f0843e3903fea51c718a00723ada7119558a40b33334750eabd2992b5ab","target":"graph","created_at":"2026-05-18T00:28:32Z","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":"In many applications, data come with a natural ordering. This ordering can often induce local dependence among nearby variables. However, in complex data, the width of this dependence may vary, making simple assumptions such as a constant neighborhood size unrealistic. We propose a framework for learning this local dependence based on estimating the inverse of the Cholesky factor of the covariance matrix. Penalized maximum likelihood estimation of this matrix yields a simple regression interpretation for local dependence in which variables are predicted by their neighbors. Our proposed method ","authors_text":"Guo Yu, Jacob Bien","cross_cats":["stat.CO","stat.ME","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2016-04-25T21:20:51Z","title":"Learning Local Dependence In Ordered Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.07451","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:fb8da59a7e61dd490a61122b53a0a2e7a1fdf212c62e90c5a04f1ac1802a03bf","target":"record","created_at":"2026-05-18T00:28:32Z","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":"b918c59a94649d432630411a99dbdefd449df36390658cedced5f8c9e437e3f1","cross_cats_sorted":["stat.CO","stat.ME","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2016-04-25T21:20:51Z","title_canon_sha256":"3a1815269c90f0deaa7864e0b016162d9e5491e9748f321166798a2ffc00ea25"},"schema_version":"1.0","source":{"id":"1604.07451","kind":"arxiv","version":3}},"canonical_sha256":"deec1f5724735adc9ffdfbc39dee18f020e3d51082e01603955a873d39163e49","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"deec1f5724735adc9ffdfbc39dee18f020e3d51082e01603955a873d39163e49","first_computed_at":"2026-05-18T00:28:32.588544Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:28:32.588544Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RlCKx1mAYdWjIsFjPKpPCD54J7Rtn6pj87fM8oYDKbEOdbtclqK/jW/qy1FXNX+z8m7ppb7kcdJq3c9/mMraAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:28:32.589496Z","signed_message":"canonical_sha256_bytes"},"source_id":"1604.07451","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fb8da59a7e61dd490a61122b53a0a2e7a1fdf212c62e90c5a04f1ac1802a03bf","sha256:52972f0843e3903fea51c718a00723ada7119558a40b33334750eabd2992b5ab"],"state_sha256":"fe486722f49fe0270ba3a9c662077957e85de9e71e71bbb65e79be99a8f31e68"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RVAuP9+kopPJXyoOR6C7x1byITQHx3/qbs5towY3HTzsOJzF3G1NxEADNlTPkvwfx78SzB7z62wsFj8rHahXDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T22:11:54.067814Z","bundle_sha256":"f9f37c48896b93d5fe707790a7ec8f8b55062fbfd049c3bee7221cf3d0775214"}}