{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:PRKZJIDXOB7CISUB5YQGNEZOMW","short_pith_number":"pith:PRKZJIDX","canonical_record":{"source":{"id":"1601.05074","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-01-19T20:42:03Z","cross_cats_sorted":[],"title_canon_sha256":"37e0d08bcadfc64deeb6db47c88f0178b42bc1e6a5a01d2697721c20ed29e258","abstract_canon_sha256":"e58e4bee0e58b738ba4497a9c216b36fe5c753dc3541ceb3aaae44af1fec5091"},"schema_version":"1.0"},"canonical_sha256":"7c5594a077707e244a81ee2066932e658f2ca592809092fac0588d0bb1e407b5","source":{"kind":"arxiv","id":"1601.05074","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1601.05074","created_at":"2026-05-18T00:15:38Z"},{"alias_kind":"arxiv_version","alias_value":"1601.05074v4","created_at":"2026-05-18T00:15:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1601.05074","created_at":"2026-05-18T00:15:38Z"},{"alias_kind":"pith_short_12","alias_value":"PRKZJIDXOB7C","created_at":"2026-05-18T12:30:39Z"},{"alias_kind":"pith_short_16","alias_value":"PRKZJIDXOB7CISUB","created_at":"2026-05-18T12:30:39Z"},{"alias_kind":"pith_short_8","alias_value":"PRKZJIDX","created_at":"2026-05-18T12:30:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:PRKZJIDXOB7CISUB5YQGNEZOMW","target":"record","payload":{"canonical_record":{"source":{"id":"1601.05074","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-01-19T20:42:03Z","cross_cats_sorted":[],"title_canon_sha256":"37e0d08bcadfc64deeb6db47c88f0178b42bc1e6a5a01d2697721c20ed29e258","abstract_canon_sha256":"e58e4bee0e58b738ba4497a9c216b36fe5c753dc3541ceb3aaae44af1fec5091"},"schema_version":"1.0"},"canonical_sha256":"7c5594a077707e244a81ee2066932e658f2ca592809092fac0588d0bb1e407b5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:38.219829Z","signature_b64":"qMeb6FH9C34HBFwvUI4S1fyTOlXs2deIbwE8Y1Zp79B6E/p21GN26EfE4tjg6yk7Gz7GVDF40k831ZRw1ZRdDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c5594a077707e244a81ee2066932e658f2ca592809092fac0588d0bb1e407b5","last_reissued_at":"2026-05-18T00:15:38.219303Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:38.219303Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1601.05074","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:15:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YPXbFcga2N09EPcQd6OfcpROruy2nYUn73mx3PAr1N8WLtDBm6pBP5ERqItdk1VOxrVyoPkNB9vWO3yprh1PCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T18:41:06.053353Z"},"content_sha256":"ca66e8f5642182e5737c208070ad07657a80ca6d788a7d01156d18a89fd0227e","schema_version":"1.0","event_id":"sha256:ca66e8f5642182e5737c208070ad07657a80ca6d788a7d01156d18a89fd0227e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:PRKZJIDXOB7CISUB5YQGNEZOMW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A systematic optimization approach for a class of statistical inference problems utilizing data augmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Dimitrios Katselis, Juan. C. Ag\\\"uero, Pedro Esc\\'arate, Rafael Orellana, Rodrigo Carvajal","submitted_at":"2016-01-19T20:42:03Z","abstract_excerpt":"We present an algorithm for a class of statistical inference problems. The main idea is to reformulate the inference problem as an optimization procedure, based on the generation of surrogate (auxiliary) functions. This approach is motivated by the MM algorithm, combined with the systematic and iterative structure of the Expectation-Maximization algorithm. The resulting algorithm can deal with hidden variables in Maximum Likelihood and Maximum a Posteriori estimation problems, Instrumental Variables, Regularized Optimization and Constrained Optimization problems.\n  The advantage of the propose"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1601.05074","kind":"arxiv","version":4},"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":"2026-05-18T00:15:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yVoc4EX9SpzdZbf2uMuOlpQckTc8HGJqD4miO1uYQzBjjdO6CuULZSP9su1ThnWVAayr1xxxKdkh3hPnXm/PDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T18:41:06.053999Z"},"content_sha256":"fa84741c30266ed0d865ad4295d7fce9f7ee97097d80df79dd4af0e6fdff9617","schema_version":"1.0","event_id":"sha256:fa84741c30266ed0d865ad4295d7fce9f7ee97097d80df79dd4af0e6fdff9617"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PRKZJIDXOB7CISUB5YQGNEZOMW/bundle.json","state_url":"https://pith.science/pith/PRKZJIDXOB7CISUB5YQGNEZOMW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PRKZJIDXOB7CISUB5YQGNEZOMW/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":"2026-05-26T18:41:06Z","links":{"resolver":"https://pith.science/pith/PRKZJIDXOB7CISUB5YQGNEZOMW","bundle":"https://pith.science/pith/PRKZJIDXOB7CISUB5YQGNEZOMW/bundle.json","state":"https://pith.science/pith/PRKZJIDXOB7CISUB5YQGNEZOMW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PRKZJIDXOB7CISUB5YQGNEZOMW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:PRKZJIDXOB7CISUB5YQGNEZOMW","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":"e58e4bee0e58b738ba4497a9c216b36fe5c753dc3541ceb3aaae44af1fec5091","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-01-19T20:42:03Z","title_canon_sha256":"37e0d08bcadfc64deeb6db47c88f0178b42bc1e6a5a01d2697721c20ed29e258"},"schema_version":"1.0","source":{"id":"1601.05074","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1601.05074","created_at":"2026-05-18T00:15:38Z"},{"alias_kind":"arxiv_version","alias_value":"1601.05074v4","created_at":"2026-05-18T00:15:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1601.05074","created_at":"2026-05-18T00:15:38Z"},{"alias_kind":"pith_short_12","alias_value":"PRKZJIDXOB7C","created_at":"2026-05-18T12:30:39Z"},{"alias_kind":"pith_short_16","alias_value":"PRKZJIDXOB7CISUB","created_at":"2026-05-18T12:30:39Z"},{"alias_kind":"pith_short_8","alias_value":"PRKZJIDX","created_at":"2026-05-18T12:30:39Z"}],"graph_snapshots":[{"event_id":"sha256:fa84741c30266ed0d865ad4295d7fce9f7ee97097d80df79dd4af0e6fdff9617","target":"graph","created_at":"2026-05-18T00:15:38Z","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 an algorithm for a class of statistical inference problems. The main idea is to reformulate the inference problem as an optimization procedure, based on the generation of surrogate (auxiliary) functions. This approach is motivated by the MM algorithm, combined with the systematic and iterative structure of the Expectation-Maximization algorithm. The resulting algorithm can deal with hidden variables in Maximum Likelihood and Maximum a Posteriori estimation problems, Instrumental Variables, Regularized Optimization and Constrained Optimization problems.\n  The advantage of the propose","authors_text":"Dimitrios Katselis, Juan. C. Ag\\\"uero, Pedro Esc\\'arate, Rafael Orellana, Rodrigo Carvajal","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-01-19T20:42:03Z","title":"A systematic optimization approach for a class of statistical inference problems utilizing data augmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1601.05074","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:ca66e8f5642182e5737c208070ad07657a80ca6d788a7d01156d18a89fd0227e","target":"record","created_at":"2026-05-18T00:15:38Z","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":"e58e4bee0e58b738ba4497a9c216b36fe5c753dc3541ceb3aaae44af1fec5091","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-01-19T20:42:03Z","title_canon_sha256":"37e0d08bcadfc64deeb6db47c88f0178b42bc1e6a5a01d2697721c20ed29e258"},"schema_version":"1.0","source":{"id":"1601.05074","kind":"arxiv","version":4}},"canonical_sha256":"7c5594a077707e244a81ee2066932e658f2ca592809092fac0588d0bb1e407b5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7c5594a077707e244a81ee2066932e658f2ca592809092fac0588d0bb1e407b5","first_computed_at":"2026-05-18T00:15:38.219303Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:38.219303Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qMeb6FH9C34HBFwvUI4S1fyTOlXs2deIbwE8Y1Zp79B6E/p21GN26EfE4tjg6yk7Gz7GVDF40k831ZRw1ZRdDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:38.219829Z","signed_message":"canonical_sha256_bytes"},"source_id":"1601.05074","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ca66e8f5642182e5737c208070ad07657a80ca6d788a7d01156d18a89fd0227e","sha256:fa84741c30266ed0d865ad4295d7fce9f7ee97097d80df79dd4af0e6fdff9617"],"state_sha256":"64b9fbc359640535a15533f081e20a27481f9e45e4ccca1c5554317ac441faa0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Qxe7qTGRbF1tYsbA2ikazFYX7WbZJCwujNxAEZ5sJQ1OxyZlVZ5Rn5nYoEdQQmYoE9C0XBvorcsU5dq/SXeoBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T18:41:06.056945Z","bundle_sha256":"a28a2a523aa4e474bfc5cab4cd75a59f0af50653bfb343933765cafbdbb2abb3"}}