{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:V3P2E55IAKCOGTXIZRUQTFRQXS","short_pith_number":"pith:V3P2E55I","canonical_record":{"source":{"id":"1501.01797","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2015-01-08T11:08:13Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"d18b2f23c258dd5f6716392dbb59f6adeb7f21e06bcc65e12001f17eaa100dab","abstract_canon_sha256":"41ee17a0ac7ed2b3ba495f840d105745ecf2d1b304109f76f699121ec3797dd9"},"schema_version":"1.0"},"canonical_sha256":"aedfa277a80284e34ee8cc69099630bc9fa07ec680583bed449eae3191799e53","source":{"kind":"arxiv","id":"1501.01797","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1501.01797","created_at":"2026-05-18T01:15:58Z"},{"alias_kind":"arxiv_version","alias_value":"1501.01797v2","created_at":"2026-05-18T01:15:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1501.01797","created_at":"2026-05-18T01:15:58Z"},{"alias_kind":"pith_short_12","alias_value":"V3P2E55IAKCO","created_at":"2026-05-18T12:29:44Z"},{"alias_kind":"pith_short_16","alias_value":"V3P2E55IAKCOGTXI","created_at":"2026-05-18T12:29:44Z"},{"alias_kind":"pith_short_8","alias_value":"V3P2E55I","created_at":"2026-05-18T12:29:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:V3P2E55IAKCOGTXIZRUQTFRQXS","target":"record","payload":{"canonical_record":{"source":{"id":"1501.01797","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2015-01-08T11:08:13Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"d18b2f23c258dd5f6716392dbb59f6adeb7f21e06bcc65e12001f17eaa100dab","abstract_canon_sha256":"41ee17a0ac7ed2b3ba495f840d105745ecf2d1b304109f76f699121ec3797dd9"},"schema_version":"1.0"},"canonical_sha256":"aedfa277a80284e34ee8cc69099630bc9fa07ec680583bed449eae3191799e53","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:15:58.880426Z","signature_b64":"7kGbyVCW2a6/W/N/N+/rZuNmi/xn8ySmi/MwUilfDrf4AmPWQC1jWi0oMRIQqL/FoPWSEpPMJFJPX6UJmGpGDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aedfa277a80284e34ee8cc69099630bc9fa07ec680583bed449eae3191799e53","last_reissued_at":"2026-05-18T01:15:58.879821Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:15:58.879821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1501.01797","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":"2026-05-18T01:15:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TYY02AyTT7eORSn56OCLk5UrTtViVEAcgYooCp5tME/l1rI76UdFHB0s+sXe042+lJZ+sW/TwrVMUOJZCnW7DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T06:44:02.128562Z"},"content_sha256":"c0d6605ef68d34596ebfbc7f63f2240835067b55edc0d0116ae7640b23f0ac6e","schema_version":"1.0","event_id":"sha256:c0d6605ef68d34596ebfbc7f63f2240835067b55edc0d0116ae7640b23f0ac6e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:V3P2E55IAKCOGTXIZRUQTFRQXS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Alyson K. Fletcher, Philip Schniter, Sundeep Rangan, Ulugbek Kamilov","submitted_at":"2015-01-08T11:08:13Z","abstract_excerpt":"Generalized Linear Models (GLMs), where a random vector $\\mathbf{x}$ is observed through a noisy, possibly nonlinear, function of a linear transform $\\mathbf{z}=\\mathbf{Ax}$ arise in a range of applications in nonlinear filtering and regression. Approximate Message Passing (AMP) methods, based on loopy belief propagation, are a promising class of approaches for approximate inference in these models. AMP methods are computationally simple, general, and admit precise analyses with testable conditions for optimality for large i.i.d. transforms $\\mathbf{A}$. However, the algorithms can easily dive"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1501.01797","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":"2026-05-18T01:15:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WSCcz4MooEg2PQtHR5jNNxjWfOV0CpVQXr+5xvAc4ulGGukgvnLK5iK1FtOTE5P3HeHRVZ1gHiYupJrxwRkjBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T06:44:02.129243Z"},"content_sha256":"dde04d85e66286cfcff3e487e9dbdeea1416288e5f1062bcb6648aa26f8ec332","schema_version":"1.0","event_id":"sha256:dde04d85e66286cfcff3e487e9dbdeea1416288e5f1062bcb6648aa26f8ec332"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V3P2E55IAKCOGTXIZRUQTFRQXS/bundle.json","state_url":"https://pith.science/pith/V3P2E55IAKCOGTXIZRUQTFRQXS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V3P2E55IAKCOGTXIZRUQTFRQXS/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":"2026-06-06T06:44:02Z","links":{"resolver":"https://pith.science/pith/V3P2E55IAKCOGTXIZRUQTFRQXS","bundle":"https://pith.science/pith/V3P2E55IAKCOGTXIZRUQTFRQXS/bundle.json","state":"https://pith.science/pith/V3P2E55IAKCOGTXIZRUQTFRQXS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V3P2E55IAKCOGTXIZRUQTFRQXS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:V3P2E55IAKCOGTXIZRUQTFRQXS","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":"41ee17a0ac7ed2b3ba495f840d105745ecf2d1b304109f76f699121ec3797dd9","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2015-01-08T11:08:13Z","title_canon_sha256":"d18b2f23c258dd5f6716392dbb59f6adeb7f21e06bcc65e12001f17eaa100dab"},"schema_version":"1.0","source":{"id":"1501.01797","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1501.01797","created_at":"2026-05-18T01:15:58Z"},{"alias_kind":"arxiv_version","alias_value":"1501.01797v2","created_at":"2026-05-18T01:15:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1501.01797","created_at":"2026-05-18T01:15:58Z"},{"alias_kind":"pith_short_12","alias_value":"V3P2E55IAKCO","created_at":"2026-05-18T12:29:44Z"},{"alias_kind":"pith_short_16","alias_value":"V3P2E55IAKCOGTXI","created_at":"2026-05-18T12:29:44Z"},{"alias_kind":"pith_short_8","alias_value":"V3P2E55I","created_at":"2026-05-18T12:29:44Z"}],"graph_snapshots":[{"event_id":"sha256:dde04d85e66286cfcff3e487e9dbdeea1416288e5f1062bcb6648aa26f8ec332","target":"graph","created_at":"2026-05-18T01:15:58Z","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":"Generalized Linear Models (GLMs), where a random vector $\\mathbf{x}$ is observed through a noisy, possibly nonlinear, function of a linear transform $\\mathbf{z}=\\mathbf{Ax}$ arise in a range of applications in nonlinear filtering and regression. Approximate Message Passing (AMP) methods, based on loopy belief propagation, are a promising class of approaches for approximate inference in these models. AMP methods are computationally simple, general, and admit precise analyses with testable conditions for optimality for large i.i.d. transforms $\\mathbf{A}$. However, the algorithms can easily dive","authors_text":"Alyson K. Fletcher, Philip Schniter, Sundeep Rangan, Ulugbek Kamilov","cross_cats":["math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2015-01-08T11:08:13Z","title":"Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1501.01797","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:c0d6605ef68d34596ebfbc7f63f2240835067b55edc0d0116ae7640b23f0ac6e","target":"record","created_at":"2026-05-18T01:15:58Z","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":"41ee17a0ac7ed2b3ba495f840d105745ecf2d1b304109f76f699121ec3797dd9","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2015-01-08T11:08:13Z","title_canon_sha256":"d18b2f23c258dd5f6716392dbb59f6adeb7f21e06bcc65e12001f17eaa100dab"},"schema_version":"1.0","source":{"id":"1501.01797","kind":"arxiv","version":2}},"canonical_sha256":"aedfa277a80284e34ee8cc69099630bc9fa07ec680583bed449eae3191799e53","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aedfa277a80284e34ee8cc69099630bc9fa07ec680583bed449eae3191799e53","first_computed_at":"2026-05-18T01:15:58.879821Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:15:58.879821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7kGbyVCW2a6/W/N/N+/rZuNmi/xn8ySmi/MwUilfDrf4AmPWQC1jWi0oMRIQqL/FoPWSEpPMJFJPX6UJmGpGDg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:15:58.880426Z","signed_message":"canonical_sha256_bytes"},"source_id":"1501.01797","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c0d6605ef68d34596ebfbc7f63f2240835067b55edc0d0116ae7640b23f0ac6e","sha256:dde04d85e66286cfcff3e487e9dbdeea1416288e5f1062bcb6648aa26f8ec332"],"state_sha256":"af12e64d42a0835560fcaa4cbba72bc604439d24a636e337d7b37adc998fd930"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T18Axdq/nWXlNyKiEh9PKpCJXBsTwqZ03saCZUSizconS5LBNEiXJLlrzjl7E/83L1I1XO5AVB3BpfrC+aFBAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T06:44:02.133011Z","bundle_sha256":"da09e71d10cf9e69f770950cc0d205f53a5d9b1bc979ce37842785ddf95ae9c9"}}