{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:R5PB4SYJ32LDVYGOC4JP5IOEOA","short_pith_number":"pith:R5PB4SYJ","canonical_record":{"source":{"id":"1808.00566","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-08-01T21:05:49Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"15aac194d13b612f9e6bdba7710f7a40ff64aa85e4d53a37be188531f4a80793","abstract_canon_sha256":"2796462bb249f80867ccebaac9ce145f24eb52f76bb6fba1542b9a6a86ee3e68"},"schema_version":"1.0"},"canonical_sha256":"8f5e1e4b09de963ae0ce1712fea1c4703a087e2bc3cac2ee8f5ca49b347872d9","source":{"kind":"arxiv","id":"1808.00566","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.00566","created_at":"2026-05-18T00:08:59Z"},{"alias_kind":"arxiv_version","alias_value":"1808.00566v2","created_at":"2026-05-18T00:08:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.00566","created_at":"2026-05-18T00:08:59Z"},{"alias_kind":"pith_short_12","alias_value":"R5PB4SYJ32LD","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"R5PB4SYJ32LDVYGO","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"R5PB4SYJ","created_at":"2026-05-18T12:32:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:R5PB4SYJ32LDVYGOC4JP5IOEOA","target":"record","payload":{"canonical_record":{"source":{"id":"1808.00566","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-08-01T21:05:49Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"15aac194d13b612f9e6bdba7710f7a40ff64aa85e4d53a37be188531f4a80793","abstract_canon_sha256":"2796462bb249f80867ccebaac9ce145f24eb52f76bb6fba1542b9a6a86ee3e68"},"schema_version":"1.0"},"canonical_sha256":"8f5e1e4b09de963ae0ce1712fea1c4703a087e2bc3cac2ee8f5ca49b347872d9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:59.535953Z","signature_b64":"tmDV7e+sn0jtwTIa1UJqFfce6UT2DtcDrC1KFxPYCpyTKS2cqJmN4xBQVud204uLujd1UkbpjJ1R/vL4j3hgDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f5e1e4b09de963ae0ce1712fea1c4703a087e2bc3cac2ee8f5ca49b347872d9","last_reissued_at":"2026-05-18T00:08:59.535154Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:59.535154Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.00566","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":"2026-05-18T00:08:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6SkXlO4Rqj4wLfHIxoBANizKyfMnPti5XEXuqRC4aNSpVo3oh1zNGfCvPhjs+LdknW7ohNiBb25zBxhEPHLxCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T23:14:19.946758Z"},"content_sha256":"e52f6ebd2a52e4ceb78e0c5a4748ec9de1364a59b28556bb83cdd74089e55a24","schema_version":"1.0","event_id":"sha256:e52f6ebd2a52e4ceb78e0c5a4748ec9de1364a59b28556bb83cdd74089e55a24"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:R5PB4SYJ32LDVYGOC4JP5IOEOA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Forest Learning from Data and its Universal Coding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Joe Suzuki","submitted_at":"2018-08-01T21:05:49Z","abstract_excerpt":"This paper considers structure learning from data with $n$ samples of $p$ variables, assuming that the structure is a forest, using the Chow-Liu algorithm. Specifically, for incomplete data, we construct two model selection algorithms that complete in $O(p^2)$ steps: one obtains a forest with the maximum posterior probability given the data, and the other obtains a forest that converges to the true one as $n$ increases. We show that the two forests are generally different when some values are missing. Additionally, we present estimations for benchmark data sets to demonstrate that both algorit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.00566","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":"2026-05-18T00:08:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z2tev0bfX7N7uEj4KItgWD5C+nocXGMl7MHyQWklRtVhbcb/23z8TYVamhKEMxaAEkKOdH9alDNWwewh5h8nBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T23:14:19.947094Z"},"content_sha256":"9af87e2319fc96bec315282695e7677ceed20dc8c106057d3dea7f5317424a51","schema_version":"1.0","event_id":"sha256:9af87e2319fc96bec315282695e7677ceed20dc8c106057d3dea7f5317424a51"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/R5PB4SYJ32LDVYGOC4JP5IOEOA/bundle.json","state_url":"https://pith.science/pith/R5PB4SYJ32LDVYGOC4JP5IOEOA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/R5PB4SYJ32LDVYGOC4JP5IOEOA/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":"2026-06-26T23:14:19Z","links":{"resolver":"https://pith.science/pith/R5PB4SYJ32LDVYGOC4JP5IOEOA","bundle":"https://pith.science/pith/R5PB4SYJ32LDVYGOC4JP5IOEOA/bundle.json","state":"https://pith.science/pith/R5PB4SYJ32LDVYGOC4JP5IOEOA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/R5PB4SYJ32LDVYGOC4JP5IOEOA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:R5PB4SYJ32LDVYGOC4JP5IOEOA","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":"2796462bb249f80867ccebaac9ce145f24eb52f76bb6fba1542b9a6a86ee3e68","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-08-01T21:05:49Z","title_canon_sha256":"15aac194d13b612f9e6bdba7710f7a40ff64aa85e4d53a37be188531f4a80793"},"schema_version":"1.0","source":{"id":"1808.00566","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.00566","created_at":"2026-05-18T00:08:59Z"},{"alias_kind":"arxiv_version","alias_value":"1808.00566v2","created_at":"2026-05-18T00:08:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.00566","created_at":"2026-05-18T00:08:59Z"},{"alias_kind":"pith_short_12","alias_value":"R5PB4SYJ32LD","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"R5PB4SYJ32LDVYGO","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"R5PB4SYJ","created_at":"2026-05-18T12:32:50Z"}],"graph_snapshots":[{"event_id":"sha256:9af87e2319fc96bec315282695e7677ceed20dc8c106057d3dea7f5317424a51","target":"graph","created_at":"2026-05-18T00:08:59Z","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":"This paper considers structure learning from data with $n$ samples of $p$ variables, assuming that the structure is a forest, using the Chow-Liu algorithm. Specifically, for incomplete data, we construct two model selection algorithms that complete in $O(p^2)$ steps: one obtains a forest with the maximum posterior probability given the data, and the other obtains a forest that converges to the true one as $n$ increases. We show that the two forests are generally different when some values are missing. Additionally, we present estimations for benchmark data sets to demonstrate that both algorit","authors_text":"Joe Suzuki","cross_cats":["math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-08-01T21:05:49Z","title":"Forest Learning from Data and its Universal Coding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.00566","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:e52f6ebd2a52e4ceb78e0c5a4748ec9de1364a59b28556bb83cdd74089e55a24","target":"record","created_at":"2026-05-18T00:08:59Z","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":"2796462bb249f80867ccebaac9ce145f24eb52f76bb6fba1542b9a6a86ee3e68","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-08-01T21:05:49Z","title_canon_sha256":"15aac194d13b612f9e6bdba7710f7a40ff64aa85e4d53a37be188531f4a80793"},"schema_version":"1.0","source":{"id":"1808.00566","kind":"arxiv","version":2}},"canonical_sha256":"8f5e1e4b09de963ae0ce1712fea1c4703a087e2bc3cac2ee8f5ca49b347872d9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8f5e1e4b09de963ae0ce1712fea1c4703a087e2bc3cac2ee8f5ca49b347872d9","first_computed_at":"2026-05-18T00:08:59.535154Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:08:59.535154Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tmDV7e+sn0jtwTIa1UJqFfce6UT2DtcDrC1KFxPYCpyTKS2cqJmN4xBQVud204uLujd1UkbpjJ1R/vL4j3hgDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:08:59.535953Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.00566","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e52f6ebd2a52e4ceb78e0c5a4748ec9de1364a59b28556bb83cdd74089e55a24","sha256:9af87e2319fc96bec315282695e7677ceed20dc8c106057d3dea7f5317424a51"],"state_sha256":"f32ba821bd0bbe962b72863555ddfb81976dcde2ac1c0f6c189d947181e399c2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qFM01lqdnozgIM5Imz9Pps+tueIBV8Hg3wIp39CSiSLWunligadvlvbIYCFj5q7iJ01WRvn9sGomF5td2TwJDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-26T23:14:19.948974Z","bundle_sha256":"81e65b2fb55a93cd8509d7d3bcc8879a2170d6fcea121d5a9cde74822fcfb0ec"}}