{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:YWLRQ4MK5YK3Q4P52RH47AOONG","short_pith_number":"pith:YWLRQ4MK","schema_version":"1.0","canonical_sha256":"c59718718aee15b871fdd44fcf81ce69a8cc57cc690a8600d83191d2e273d860","source":{"kind":"arxiv","id":"1110.4355","version":1},"attestation_state":"computed","paper":{"title":"Sequential Detection with Mutual Information Stopping Cost","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Erik Miehling, Michel Gevers, Robert Bitmead, Vikram Krishnamurthy","submitted_at":"2011-10-19T19:16:26Z","abstract_excerpt":"This paper formulates and solves a sequential detection problem that involves the mutual information (stochastic observability) of a Gaussian process observed in noise with missing measurements. The main result is that the optimal decision is characterized by a monotone policy on the partially ordered set of positive definite covariance matrices. This monotone structure implies that numerically efficient algorithms can be designed to estimate and implement monotone parametrized decision policies.The sequential detection problem is motivated by applications in radar scheduling where the aim is "},"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":"1110.4355","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2011-10-19T19:16:26Z","cross_cats_sorted":[],"title_canon_sha256":"ba76ae9f0e5e776bcbff5db956dbf0498648e68eb0cb55387f357dc47c3f8afa","abstract_canon_sha256":"22df7b2e6a13c4bf359c764d90cb00aa72d0655f7032cf433ef6bc7dda799616"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:00:08.444378Z","signature_b64":"gACTQPTDUQCs3lnoCNA+JiIk/y7tYdgx84UO/vPYaeYZgax+L/tld0qDHTUsLRAeXoygKTRTluKs0FUJmHbWCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c59718718aee15b871fdd44fcf81ce69a8cc57cc690a8600d83191d2e273d860","last_reissued_at":"2026-05-18T02:00:08.443804Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:00:08.443804Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sequential Detection with Mutual Information Stopping Cost","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Erik Miehling, Michel Gevers, Robert Bitmead, Vikram Krishnamurthy","submitted_at":"2011-10-19T19:16:26Z","abstract_excerpt":"This paper formulates and solves a sequential detection problem that involves the mutual information (stochastic observability) of a Gaussian process observed in noise with missing measurements. The main result is that the optimal decision is characterized by a monotone policy on the partially ordered set of positive definite covariance matrices. This monotone structure implies that numerically efficient algorithms can be designed to estimate and implement monotone parametrized decision policies.The sequential detection problem is motivated by applications in radar scheduling where the aim is "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1110.4355","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1110.4355","created_at":"2026-05-18T02:00:08.443887+00:00"},{"alias_kind":"arxiv_version","alias_value":"1110.4355v1","created_at":"2026-05-18T02:00:08.443887+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1110.4355","created_at":"2026-05-18T02:00:08.443887+00:00"},{"alias_kind":"pith_short_12","alias_value":"YWLRQ4MK5YK3","created_at":"2026-05-18T12:26:47.523578+00:00"},{"alias_kind":"pith_short_16","alias_value":"YWLRQ4MK5YK3Q4P5","created_at":"2026-05-18T12:26:47.523578+00:00"},{"alias_kind":"pith_short_8","alias_value":"YWLRQ4MK","created_at":"2026-05-18T12:26:47.523578+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/YWLRQ4MK5YK3Q4P52RH47AOONG","json":"https://pith.science/pith/YWLRQ4MK5YK3Q4P52RH47AOONG.json","graph_json":"https://pith.science/api/pith-number/YWLRQ4MK5YK3Q4P52RH47AOONG/graph.json","events_json":"https://pith.science/api/pith-number/YWLRQ4MK5YK3Q4P52RH47AOONG/events.json","paper":"https://pith.science/paper/YWLRQ4MK"},"agent_actions":{"view_html":"https://pith.science/pith/YWLRQ4MK5YK3Q4P52RH47AOONG","download_json":"https://pith.science/pith/YWLRQ4MK5YK3Q4P52RH47AOONG.json","view_paper":"https://pith.science/paper/YWLRQ4MK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1110.4355&json=true","fetch_graph":"https://pith.science/api/pith-number/YWLRQ4MK5YK3Q4P52RH47AOONG/graph.json","fetch_events":"https://pith.science/api/pith-number/YWLRQ4MK5YK3Q4P52RH47AOONG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YWLRQ4MK5YK3Q4P52RH47AOONG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YWLRQ4MK5YK3Q4P52RH47AOONG/action/storage_attestation","attest_author":"https://pith.science/pith/YWLRQ4MK5YK3Q4P52RH47AOONG/action/author_attestation","sign_citation":"https://pith.science/pith/YWLRQ4MK5YK3Q4P52RH47AOONG/action/citation_signature","submit_replication":"https://pith.science/pith/YWLRQ4MK5YK3Q4P52RH47AOONG/action/replication_record"}},"created_at":"2026-05-18T02:00:08.443887+00:00","updated_at":"2026-05-18T02:00:08.443887+00:00"}