{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:UQW34AT6YR2FOZ75MLPYDXRW7N","short_pith_number":"pith:UQW34AT6","schema_version":"1.0","canonical_sha256":"a42dbe027ec4745767fd62df81de36fb4a21dc02eb91bdca3a4e1b4149be8b83","source":{"kind":"arxiv","id":"2111.13246","version":1},"attestation_state":"computed","paper":{"title":"Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","cs.SY","math.NA"],"primary_cat":"eess.SY","authors_text":"Akil Narayan, Christopher Beattie, Elizabeth Qian, Jemima M. Tabeart, Jiahua Jiang, Peter R. Kramer, Serkan Gugercin","submitted_at":"2021-11-25T20:45:55Z","abstract_excerpt":"We consider the Bayesian approach to the linear Gaussian inference problem of inferring the initial condition of a linear dynamical system from noisy output measurements taken after the initial time. In practical applications, the large dimension of the dynamical system state poses a computational obstacle to computing the exact posterior distribution. Model reduction offers a variety of computational tools that seek to reduce this computational burden. In particular, balanced truncation is a system-theoretic approach to model reduction which obtains an efficient reduced-dimension dynamical sy"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2111.13246","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2021-11-25T20:45:55Z","cross_cats_sorted":["cs.NA","cs.SY","math.NA"],"title_canon_sha256":"408411fb4b07165d4514c04af755a935ab73f29baee7ddb1b71d027b4004b175","abstract_canon_sha256":"312cdc8b1e2f2a97f8c62448c2d52ddde8884abd55b209e069eeb67df96bbbe4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:35:19.250422Z","signature_b64":"VQ9/PzIfTUfuFPKluTv79jV4SsCz2tYuD8Q7Oc2/UlElJsXNSgb38rddes91dP/vMIb0Vezk1T5bvXuR6Zp2CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a42dbe027ec4745767fd62df81de36fb4a21dc02eb91bdca3a4e1b4149be8b83","last_reissued_at":"2026-07-05T03:35:19.249940Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:35:19.249940Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","cs.SY","math.NA"],"primary_cat":"eess.SY","authors_text":"Akil Narayan, Christopher Beattie, Elizabeth Qian, Jemima M. Tabeart, Jiahua Jiang, Peter R. Kramer, Serkan Gugercin","submitted_at":"2021-11-25T20:45:55Z","abstract_excerpt":"We consider the Bayesian approach to the linear Gaussian inference problem of inferring the initial condition of a linear dynamical system from noisy output measurements taken after the initial time. In practical applications, the large dimension of the dynamical system state poses a computational obstacle to computing the exact posterior distribution. Model reduction offers a variety of computational tools that seek to reduce this computational burden. In particular, balanced truncation is a system-theoretic approach to model reduction which obtains an efficient reduced-dimension dynamical sy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.13246","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2111.13246/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2111.13246","created_at":"2026-07-05T03:35:19.249998+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.13246v1","created_at":"2026-07-05T03:35:19.249998+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.13246","created_at":"2026-07-05T03:35:19.249998+00:00"},{"alias_kind":"pith_short_12","alias_value":"UQW34AT6YR2F","created_at":"2026-07-05T03:35:19.249998+00:00"},{"alias_kind":"pith_short_16","alias_value":"UQW34AT6YR2FOZ75","created_at":"2026-07-05T03:35:19.249998+00:00"},{"alias_kind":"pith_short_8","alias_value":"UQW34AT6","created_at":"2026-07-05T03:35:19.249998+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UQW34AT6YR2FOZ75MLPYDXRW7N","json":"https://pith.science/pith/UQW34AT6YR2FOZ75MLPYDXRW7N.json","graph_json":"https://pith.science/api/pith-number/UQW34AT6YR2FOZ75MLPYDXRW7N/graph.json","events_json":"https://pith.science/api/pith-number/UQW34AT6YR2FOZ75MLPYDXRW7N/events.json","paper":"https://pith.science/paper/UQW34AT6"},"agent_actions":{"view_html":"https://pith.science/pith/UQW34AT6YR2FOZ75MLPYDXRW7N","download_json":"https://pith.science/pith/UQW34AT6YR2FOZ75MLPYDXRW7N.json","view_paper":"https://pith.science/paper/UQW34AT6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.13246&json=true","fetch_graph":"https://pith.science/api/pith-number/UQW34AT6YR2FOZ75MLPYDXRW7N/graph.json","fetch_events":"https://pith.science/api/pith-number/UQW34AT6YR2FOZ75MLPYDXRW7N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UQW34AT6YR2FOZ75MLPYDXRW7N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UQW34AT6YR2FOZ75MLPYDXRW7N/action/storage_attestation","attest_author":"https://pith.science/pith/UQW34AT6YR2FOZ75MLPYDXRW7N/action/author_attestation","sign_citation":"https://pith.science/pith/UQW34AT6YR2FOZ75MLPYDXRW7N/action/citation_signature","submit_replication":"https://pith.science/pith/UQW34AT6YR2FOZ75MLPYDXRW7N/action/replication_record"}},"created_at":"2026-07-05T03:35:19.249998+00:00","updated_at":"2026-07-05T03:35:19.249998+00:00"}