{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:O4TLSUJLTXST6KZNLRO3JPF34K","short_pith_number":"pith:O4TLSUJL","canonical_record":{"source":{"id":"1409.8109","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2014-09-29T13:15:29Z","cross_cats_sorted":["math.NA"],"title_canon_sha256":"ca5a032dd35b9d5a15c1e2bfb1925fc3cf7808689d0fe71797f207e3608465d0","abstract_canon_sha256":"d95a999091acffb14d4ebd3b6baf4d2cd5dfb5a08bc55de26c83a3dfd75cf985"},"schema_version":"1.0"},"canonical_sha256":"7726b9512b9de53f2b2d5c5db4bcbbe2b2719182f82b15c794bf79edf342680b","source":{"kind":"arxiv","id":"1409.8109","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.8109","created_at":"2026-05-18T02:38:36Z"},{"alias_kind":"arxiv_version","alias_value":"1409.8109v1","created_at":"2026-05-18T02:38:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.8109","created_at":"2026-05-18T02:38:36Z"},{"alias_kind":"pith_short_12","alias_value":"O4TLSUJLTXST","created_at":"2026-05-18T12:28:41Z"},{"alias_kind":"pith_short_16","alias_value":"O4TLSUJLTXST6KZN","created_at":"2026-05-18T12:28:41Z"},{"alias_kind":"pith_short_8","alias_value":"O4TLSUJL","created_at":"2026-05-18T12:28:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:O4TLSUJLTXST6KZNLRO3JPF34K","target":"record","payload":{"canonical_record":{"source":{"id":"1409.8109","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2014-09-29T13:15:29Z","cross_cats_sorted":["math.NA"],"title_canon_sha256":"ca5a032dd35b9d5a15c1e2bfb1925fc3cf7808689d0fe71797f207e3608465d0","abstract_canon_sha256":"d95a999091acffb14d4ebd3b6baf4d2cd5dfb5a08bc55de26c83a3dfd75cf985"},"schema_version":"1.0"},"canonical_sha256":"7726b9512b9de53f2b2d5c5db4bcbbe2b2719182f82b15c794bf79edf342680b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:38:36.517616Z","signature_b64":"EbnnN5duQKKh2/TWsztZnxnmh6j56YRYOJL4SvYD/9/Bio6Xg0oBXm3UbnGGtTb8HrG6LNnW05KFbAbtVPSbDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7726b9512b9de53f2b2d5c5db4bcbbe2b2719182f82b15c794bf79edf342680b","last_reissued_at":"2026-05-18T02:38:36.516861Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:38:36.516861Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1409.8109","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":"2026-05-18T02:38:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OrHCKWM+YYkdbf63wGv78R+pfu6whMcv3+MwBLAJI/HXkH22luUIuQu/oS3onn0/Hqw7sZSJ3VxTiE1W9BXBAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T13:21:34.585987Z"},"content_sha256":"3aafef539471fb5476eb94722d96c9db35094cc6cf1c9b9c19bb10027786f3d8","schema_version":"1.0","event_id":"sha256:3aafef539471fb5476eb94722d96c9db35094cc6cf1c9b9c19bb10027786f3d8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:O4TLSUJLTXST6KZNLRO3JPF34K","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sequential Monte Carlo samplers for semilinear inverse problems and application to magnetoencephalography","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.NA"],"primary_cat":"stat.AP","authors_text":"Alberto Sorrentino, Sara Sommariva","submitted_at":"2014-09-29T13:15:29Z","abstract_excerpt":"We discuss the use of a recent class of sequential Monte Carlo methods for solving inverse problems characterized by a semi-linear structure, i.e. where the data depend linearly on a subset of variables and nonlinearly on the remaining ones. In this type of problems, under proper Gaussian assumptions one can marginalize the linear variables. This means that the Monte Carlo procedure needs only to be applied to the nonlinear variables, while the linear ones can be treated analytically; as a result, the Monte Carlo variance and/or the computational cost decrease. We use this approach to solve th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.8109","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":"2026-05-18T02:38:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iSy/U3RumFmfF/LJ3vE+T11rpBUn6siuodlR5IV/5tF5ltDVKGLNLVnoTBDNoJ2Q6RNCo9p4eKwlAoD39GuhCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T13:21:34.586328Z"},"content_sha256":"9b8c189be7b2333fdf41c7d9f26ce226ca26d3efe03be2451a1e15f8a35f9435","schema_version":"1.0","event_id":"sha256:9b8c189be7b2333fdf41c7d9f26ce226ca26d3efe03be2451a1e15f8a35f9435"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O4TLSUJLTXST6KZNLRO3JPF34K/bundle.json","state_url":"https://pith.science/pith/O4TLSUJLTXST6KZNLRO3JPF34K/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O4TLSUJLTXST6KZNLRO3JPF34K/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":"2026-06-03T13:21:34Z","links":{"resolver":"https://pith.science/pith/O4TLSUJLTXST6KZNLRO3JPF34K","bundle":"https://pith.science/pith/O4TLSUJLTXST6KZNLRO3JPF34K/bundle.json","state":"https://pith.science/pith/O4TLSUJLTXST6KZNLRO3JPF34K/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O4TLSUJLTXST6KZNLRO3JPF34K/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:O4TLSUJLTXST6KZNLRO3JPF34K","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":"d95a999091acffb14d4ebd3b6baf4d2cd5dfb5a08bc55de26c83a3dfd75cf985","cross_cats_sorted":["math.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2014-09-29T13:15:29Z","title_canon_sha256":"ca5a032dd35b9d5a15c1e2bfb1925fc3cf7808689d0fe71797f207e3608465d0"},"schema_version":"1.0","source":{"id":"1409.8109","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.8109","created_at":"2026-05-18T02:38:36Z"},{"alias_kind":"arxiv_version","alias_value":"1409.8109v1","created_at":"2026-05-18T02:38:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.8109","created_at":"2026-05-18T02:38:36Z"},{"alias_kind":"pith_short_12","alias_value":"O4TLSUJLTXST","created_at":"2026-05-18T12:28:41Z"},{"alias_kind":"pith_short_16","alias_value":"O4TLSUJLTXST6KZN","created_at":"2026-05-18T12:28:41Z"},{"alias_kind":"pith_short_8","alias_value":"O4TLSUJL","created_at":"2026-05-18T12:28:41Z"}],"graph_snapshots":[{"event_id":"sha256:9b8c189be7b2333fdf41c7d9f26ce226ca26d3efe03be2451a1e15f8a35f9435","target":"graph","created_at":"2026-05-18T02:38:36Z","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 discuss the use of a recent class of sequential Monte Carlo methods for solving inverse problems characterized by a semi-linear structure, i.e. where the data depend linearly on a subset of variables and nonlinearly on the remaining ones. In this type of problems, under proper Gaussian assumptions one can marginalize the linear variables. This means that the Monte Carlo procedure needs only to be applied to the nonlinear variables, while the linear ones can be treated analytically; as a result, the Monte Carlo variance and/or the computational cost decrease. We use this approach to solve th","authors_text":"Alberto Sorrentino, Sara Sommariva","cross_cats":["math.NA"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2014-09-29T13:15:29Z","title":"Sequential Monte Carlo samplers for semilinear inverse problems and application to magnetoencephalography"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.8109","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":"sha256:3aafef539471fb5476eb94722d96c9db35094cc6cf1c9b9c19bb10027786f3d8","target":"record","created_at":"2026-05-18T02:38:36Z","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":"d95a999091acffb14d4ebd3b6baf4d2cd5dfb5a08bc55de26c83a3dfd75cf985","cross_cats_sorted":["math.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2014-09-29T13:15:29Z","title_canon_sha256":"ca5a032dd35b9d5a15c1e2bfb1925fc3cf7808689d0fe71797f207e3608465d0"},"schema_version":"1.0","source":{"id":"1409.8109","kind":"arxiv","version":1}},"canonical_sha256":"7726b9512b9de53f2b2d5c5db4bcbbe2b2719182f82b15c794bf79edf342680b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7726b9512b9de53f2b2d5c5db4bcbbe2b2719182f82b15c794bf79edf342680b","first_computed_at":"2026-05-18T02:38:36.516861Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:38:36.516861Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EbnnN5duQKKh2/TWsztZnxnmh6j56YRYOJL4SvYD/9/Bio6Xg0oBXm3UbnGGtTb8HrG6LNnW05KFbAbtVPSbDw==","signature_status":"signed_v1","signed_at":"2026-05-18T02:38:36.517616Z","signed_message":"canonical_sha256_bytes"},"source_id":"1409.8109","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3aafef539471fb5476eb94722d96c9db35094cc6cf1c9b9c19bb10027786f3d8","sha256:9b8c189be7b2333fdf41c7d9f26ce226ca26d3efe03be2451a1e15f8a35f9435"],"state_sha256":"935779626e0e0893583047ff356a5bbf29993e7aae8f372cfb08eb543d8d257c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UEI0Qu9/tZExqIg9LcZ6BBhGwgNW48z1DLdsvCTh2JGli2DuHvdkfQnDQ5q6s67BRsz+393V1PLQNLUq849jCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T13:21:34.588249Z","bundle_sha256":"e3b728be48fdbd98ced4518d17164b60eec5c9adf8c767e94cd8667a06593773"}}