{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:ACDI7UNN34NE6D3UZ6IH5AT3EE","short_pith_number":"pith:ACDI7UNN","schema_version":"1.0","canonical_sha256":"00868fd1addf1a4f0f74cf907e827b211538d18d83926f3b93b36ddce7c99704","source":{"kind":"arxiv","id":"1604.08320","version":1},"attestation_state":"computed","paper":{"title":"Sequential Bayesian optimal experimental design via approximate dynamic programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.CO","stat.ML"],"primary_cat":"stat.ME","authors_text":"Xun Huan, Youssef M. Marzouk","submitted_at":"2016-04-28T06:32:27Z","abstract_excerpt":"The design of multiple experiments is commonly undertaken via suboptimal strategies, such as batch (open-loop) design that omits feedback or greedy (myopic) design that does not account for future effects. This paper introduces new strategies for the optimal design of sequential experiments. First, we rigorously formulate the general sequential optimal experimental design (sOED) problem as a dynamic program. Batch and greedy designs are shown to result from special cases of this formulation. We then focus on sOED for parameter inference, adopting a Bayesian formulation with an information theo"},"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":"1604.08320","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-04-28T06:32:27Z","cross_cats_sorted":["math.OC","stat.CO","stat.ML"],"title_canon_sha256":"05d829570a642af6c29cf2b99452d11908c3b00f121b306b5c9b033643b8f4bc","abstract_canon_sha256":"9ae5c5c03a272136d151f733b7f767b810ab4687f3ea0fe5f73f7783d7316bef"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:16:04.317854Z","signature_b64":"u1grQfZfzCldtQRXI0L429OfbVN0XmxP6+OilqK7lS5NKaguEMP4RTyeJ3F2DYLCzrLh2/ni6aJatM2rCXZ8Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"00868fd1addf1a4f0f74cf907e827b211538d18d83926f3b93b36ddce7c99704","last_reissued_at":"2026-05-18T01:16:04.317103Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:16:04.317103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sequential Bayesian optimal experimental design via approximate dynamic programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.CO","stat.ML"],"primary_cat":"stat.ME","authors_text":"Xun Huan, Youssef M. Marzouk","submitted_at":"2016-04-28T06:32:27Z","abstract_excerpt":"The design of multiple experiments is commonly undertaken via suboptimal strategies, such as batch (open-loop) design that omits feedback or greedy (myopic) design that does not account for future effects. This paper introduces new strategies for the optimal design of sequential experiments. First, we rigorously formulate the general sequential optimal experimental design (sOED) problem as a dynamic program. Batch and greedy designs are shown to result from special cases of this formulation. We then focus on sOED for parameter inference, adopting a Bayesian formulation with an information theo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.08320","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":"1604.08320","created_at":"2026-05-18T01:16:04.317223+00:00"},{"alias_kind":"arxiv_version","alias_value":"1604.08320v1","created_at":"2026-05-18T01:16:04.317223+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.08320","created_at":"2026-05-18T01:16:04.317223+00:00"},{"alias_kind":"pith_short_12","alias_value":"ACDI7UNN34NE","created_at":"2026-05-18T12:30:07.202191+00:00"},{"alias_kind":"pith_short_16","alias_value":"ACDI7UNN34NE6D3U","created_at":"2026-05-18T12:30:07.202191+00:00"},{"alias_kind":"pith_short_8","alias_value":"ACDI7UNN","created_at":"2026-05-18T12:30:07.202191+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":4,"sample":[{"citing_arxiv_id":"2306.10430","citing_title":"Variational Sequential Optimal Experimental Design using Reinforcement Learning","ref_index":20,"is_internal_anchor":true},{"citing_arxiv_id":"2407.16212","citing_title":"Optimal experimental design: Formulations and computations","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"2502.20086","citing_title":"Subspace accelerated measure transport methods for fast and scalable sequential experimental design, with application to photoacoustic imaging","ref_index":34,"is_internal_anchor":true},{"citing_arxiv_id":"2605.12899","citing_title":"Robust Sequential Experimental Design for A/B Testing","ref_index":8,"is_internal_anchor":true},{"citing_arxiv_id":"2604.25193","citing_title":"Adaptive Sensing beyond Non-Adaptive Information Limits: End-to-End Co-Design of Geometry, Policy, and Inference","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08812","citing_title":"Sensor Placement for Tsunami Early Warning via Large-Scale Bayesian Optimal Experimental Design","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ACDI7UNN34NE6D3UZ6IH5AT3EE","json":"https://pith.science/pith/ACDI7UNN34NE6D3UZ6IH5AT3EE.json","graph_json":"https://pith.science/api/pith-number/ACDI7UNN34NE6D3UZ6IH5AT3EE/graph.json","events_json":"https://pith.science/api/pith-number/ACDI7UNN34NE6D3UZ6IH5AT3EE/events.json","paper":"https://pith.science/paper/ACDI7UNN"},"agent_actions":{"view_html":"https://pith.science/pith/ACDI7UNN34NE6D3UZ6IH5AT3EE","download_json":"https://pith.science/pith/ACDI7UNN34NE6D3UZ6IH5AT3EE.json","view_paper":"https://pith.science/paper/ACDI7UNN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1604.08320&json=true","fetch_graph":"https://pith.science/api/pith-number/ACDI7UNN34NE6D3UZ6IH5AT3EE/graph.json","fetch_events":"https://pith.science/api/pith-number/ACDI7UNN34NE6D3UZ6IH5AT3EE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ACDI7UNN34NE6D3UZ6IH5AT3EE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ACDI7UNN34NE6D3UZ6IH5AT3EE/action/storage_attestation","attest_author":"https://pith.science/pith/ACDI7UNN34NE6D3UZ6IH5AT3EE/action/author_attestation","sign_citation":"https://pith.science/pith/ACDI7UNN34NE6D3UZ6IH5AT3EE/action/citation_signature","submit_replication":"https://pith.science/pith/ACDI7UNN34NE6D3UZ6IH5AT3EE/action/replication_record"}},"created_at":"2026-05-18T01:16:04.317223+00:00","updated_at":"2026-05-18T01:16:04.317223+00:00"}