{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sequential-bayesian-optimal-experimental","title":"Sequential Bayesian optimal experimental design via approximate dynamic programming","arxiv_id":"1604.08320","date":"2016-04-28","proceeding":null,"authors":["Xun Huan","Youssef M. Marzouk"],"abstract":"The design of multiple experiments is commonly undertaken via suboptimal\nstrategies, such as batch (open-loop) design that omits feedback or greedy\n(myopic) design that does not account for future effects. This paper introduces\nnew strategies for the optimal design of sequential experiments. First, we\nrigorously formulate the general sequential optimal experimental design (sOED)\nproblem as a dynamic program. Batch and greedy designs are shown to result from\nspecial cases of this formulation. We then focus on sOED for parameter\ninference, adopting a Bayesian formulation with an information theoretic design\nobjective. To make the problem tractable, we develop new numerical approaches\nfor nonlinear design with continuous parameter, design, and observation spaces.\nWe approximate the optimal policy by using backward induction with regression\nto construct and refine value function approximations in the dynamic program.\nThe proposed algorithm iteratively generates trajectories via exploration and\nexploitation to improve approximation accuracy in frequently visited regions of\nthe state space. Numerical results are verified against analytical solutions in\na linear-Gaussian setting. Advantages over batch and greedy design are then\ndemonstrated on a nonlinear source inversion problem where we seek an optimal\npolicy for sequential sensing.","url_abs":"http://arxiv.org/abs/1604.08320v1","url_pdf":"http://arxiv.org/pdf/1604.08320v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sequential-bayesian-optimal-experimental","repo_url":"https://github.com/wgshen/sOED","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"experimental-design","task_name":"Experimental Design"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.08320","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1604.08320"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/wgshen/sOED","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":3},"by_repo_kind":{"listed":{"samples":3,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"9a3e8f266e439caf","entry":"norm_logpdf","repo":"wgshen/sOED","repo_kind":"listed","path":"sOED/utils.py","file_url":"https://github.com/wgshen/sOED/blob/HEAD/sOED/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9a3e8f266e439caf"}},{"code_sha256_prefix":"c3687e7ad5d14c4f","entry":"norm_pdf","repo":"wgshen/sOED","repo_kind":"listed","path":"sOED/utils.py","file_url":"https://github.com/wgshen/sOED/blob/HEAD/sOED/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c3687e7ad5d14c4f"}},{"code_sha256_prefix":"1a7c50f177398917","entry":"uniform_logpdf","repo":"wgshen/sOED","repo_kind":"listed","path":"sOED/utils.py","file_url":"https://github.com/wgshen/sOED/blob/HEAD/sOED/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1a7c50f177398917"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}