{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:RMDLS3DYKGM7WUY44AH26PKK5X","short_pith_number":"pith:RMDLS3DY","canonical_record":{"source":{"id":"1611.09293","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2016-11-25T12:50:57Z","cross_cats_sorted":["math.PR"],"title_canon_sha256":"7e87102303fbc9cdd3126bf32a60d0a72238c50734f1bfb97feffd654d29e3d3","abstract_canon_sha256":"211619c8abc666b61137c65513605270c3d8d3501ff7a2c5f83ceec0140c1d07"},"schema_version":"1.0"},"canonical_sha256":"8b06b96c785199fb531ce00faf3d4aedd4101c683b5c1155fdcf9ca922b9525d","source":{"kind":"arxiv","id":"1611.09293","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.09293","created_at":"2026-05-18T00:56:28Z"},{"alias_kind":"arxiv_version","alias_value":"1611.09293v1","created_at":"2026-05-18T00:56:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.09293","created_at":"2026-05-18T00:56:28Z"},{"alias_kind":"pith_short_12","alias_value":"RMDLS3DYKGM7","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_16","alias_value":"RMDLS3DYKGM7WUY4","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_8","alias_value":"RMDLS3DY","created_at":"2026-05-18T12:30:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:RMDLS3DYKGM7WUY44AH26PKK5X","target":"record","payload":{"canonical_record":{"source":{"id":"1611.09293","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2016-11-25T12:50:57Z","cross_cats_sorted":["math.PR"],"title_canon_sha256":"7e87102303fbc9cdd3126bf32a60d0a72238c50734f1bfb97feffd654d29e3d3","abstract_canon_sha256":"211619c8abc666b61137c65513605270c3d8d3501ff7a2c5f83ceec0140c1d07"},"schema_version":"1.0"},"canonical_sha256":"8b06b96c785199fb531ce00faf3d4aedd4101c683b5c1155fdcf9ca922b9525d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:56:28.573220Z","signature_b64":"CahamBxG6VHlcgwfWXJZI+nEm6GWf0PSgRWRDU9wPupgx+byqoXlblCUax2EVS9vmNXo7nAiNzean8jNvnQpAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b06b96c785199fb531ce00faf3d4aedd4101c683b5c1155fdcf9ca922b9525d","last_reissued_at":"2026-05-18T00:56:28.572712Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:56:28.572712Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.09293","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":"2026-05-18T00:56:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7y+/2g7nmf/+xBO3kW6iHBa/uJSdGIAJn2QXdu9cIcAlXM0sQzVp96Dwv4D2DE7+fDf0cQ9hJHoG64oUfeqyDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T14:55:31.451077Z"},"content_sha256":"c8ef88a6c60efd8b5112b833598a0e950be13b29016410ef5b8adf27f087b84b","schema_version":"1.0","event_id":"sha256:c8ef88a6c60efd8b5112b833598a0e950be13b29016410ef5b8adf27f087b84b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:RMDLS3DYKGM7WUY44AH26PKK5X","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian Parameter Estimation via Filtering and Functional Approximations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR"],"primary_cat":"math.NA","authors_text":"Alexander Litvinenko, Bojana V. Rosic, Elmar Zander, Hermann G. Matthies","submitted_at":"2016-11-25T12:50:57Z","abstract_excerpt":"The inverse problem of determining parameters in a model by comparing some output of the model with observations is addressed. This is a description for what hat to be done to use the Gauss-Markov-Kalman filter for the Bayesian estimation and updating of parameters in a computational model. This is a filter acting on random variables, and while its Monte Carlo variant --- the Ensemble Kalman Filter (EnKF) --- is fairly straightforward, we subsequently only sketch its implementation with the help of functional representations."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.09293","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":"2026-05-18T00:56:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wXtzAfIobuN00d1VtBPjyGUoTtzjuX3NpPp7UoKRDBS23RfDG5IvneoCDnw/39+IuNjnV05/a+ncB5ojFpF5Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T14:55:31.451437Z"},"content_sha256":"7ed7efaa405e7efc288b57daaa4ec1480d7e72008bb1e5c756dff1839e6dd04d","schema_version":"1.0","event_id":"sha256:7ed7efaa405e7efc288b57daaa4ec1480d7e72008bb1e5c756dff1839e6dd04d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RMDLS3DYKGM7WUY44AH26PKK5X/bundle.json","state_url":"https://pith.science/pith/RMDLS3DYKGM7WUY44AH26PKK5X/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RMDLS3DYKGM7WUY44AH26PKK5X/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":"2026-06-09T14:55:31Z","links":{"resolver":"https://pith.science/pith/RMDLS3DYKGM7WUY44AH26PKK5X","bundle":"https://pith.science/pith/RMDLS3DYKGM7WUY44AH26PKK5X/bundle.json","state":"https://pith.science/pith/RMDLS3DYKGM7WUY44AH26PKK5X/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RMDLS3DYKGM7WUY44AH26PKK5X/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:RMDLS3DYKGM7WUY44AH26PKK5X","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":"211619c8abc666b61137c65513605270c3d8d3501ff7a2c5f83ceec0140c1d07","cross_cats_sorted":["math.PR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2016-11-25T12:50:57Z","title_canon_sha256":"7e87102303fbc9cdd3126bf32a60d0a72238c50734f1bfb97feffd654d29e3d3"},"schema_version":"1.0","source":{"id":"1611.09293","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.09293","created_at":"2026-05-18T00:56:28Z"},{"alias_kind":"arxiv_version","alias_value":"1611.09293v1","created_at":"2026-05-18T00:56:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.09293","created_at":"2026-05-18T00:56:28Z"},{"alias_kind":"pith_short_12","alias_value":"RMDLS3DYKGM7","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_16","alias_value":"RMDLS3DYKGM7WUY4","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_8","alias_value":"RMDLS3DY","created_at":"2026-05-18T12:30:41Z"}],"graph_snapshots":[{"event_id":"sha256:7ed7efaa405e7efc288b57daaa4ec1480d7e72008bb1e5c756dff1839e6dd04d","target":"graph","created_at":"2026-05-18T00:56:28Z","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":"The inverse problem of determining parameters in a model by comparing some output of the model with observations is addressed. This is a description for what hat to be done to use the Gauss-Markov-Kalman filter for the Bayesian estimation and updating of parameters in a computational model. This is a filter acting on random variables, and while its Monte Carlo variant --- the Ensemble Kalman Filter (EnKF) --- is fairly straightforward, we subsequently only sketch its implementation with the help of functional representations.","authors_text":"Alexander Litvinenko, Bojana V. Rosic, Elmar Zander, Hermann G. Matthies","cross_cats":["math.PR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2016-11-25T12:50:57Z","title":"Bayesian Parameter Estimation via Filtering and Functional Approximations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.09293","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:c8ef88a6c60efd8b5112b833598a0e950be13b29016410ef5b8adf27f087b84b","target":"record","created_at":"2026-05-18T00:56:28Z","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":"211619c8abc666b61137c65513605270c3d8d3501ff7a2c5f83ceec0140c1d07","cross_cats_sorted":["math.PR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2016-11-25T12:50:57Z","title_canon_sha256":"7e87102303fbc9cdd3126bf32a60d0a72238c50734f1bfb97feffd654d29e3d3"},"schema_version":"1.0","source":{"id":"1611.09293","kind":"arxiv","version":1}},"canonical_sha256":"8b06b96c785199fb531ce00faf3d4aedd4101c683b5c1155fdcf9ca922b9525d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8b06b96c785199fb531ce00faf3d4aedd4101c683b5c1155fdcf9ca922b9525d","first_computed_at":"2026-05-18T00:56:28.572712Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:56:28.572712Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CahamBxG6VHlcgwfWXJZI+nEm6GWf0PSgRWRDU9wPupgx+byqoXlblCUax2EVS9vmNXo7nAiNzean8jNvnQpAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:56:28.573220Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.09293","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c8ef88a6c60efd8b5112b833598a0e950be13b29016410ef5b8adf27f087b84b","sha256:7ed7efaa405e7efc288b57daaa4ec1480d7e72008bb1e5c756dff1839e6dd04d"],"state_sha256":"e1925b6e7e962a546b1d840f6f31ef20a1b25c6ef185f4d49c0f947ead307b52"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cYFQVA9v4VheN03dIrLoZkuILMzoUqFXSb7yFIL16Zl3CgV2HCj3+A3kCoUu4YzN4Nif3dd1EqvRU+tcKURqAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T14:55:31.453464Z","bundle_sha256":"cd1c7a0fd1cd5baef60a9d0ce87a5c94e739a4d12e448a36f1406fc99019521d"}}