{"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/efficient-bayesian-experimental-design-for","title":"Efficient Bayesian Experimental Design for Implicit Models","arxiv_id":"1810.09912","date":"2018-10-23","proceeding":null,"authors":["Steven Kleinegesse","Michael Gutmann"],"abstract":"Bayesian experimental design involves the optimal allocation of resources in\nan experiment, with the aim of optimising cost and performance. For implicit\nmodels, where the likelihood is intractable but sampling from the model is\npossible, this task is particularly difficult and therefore largely unexplored.\nThis is mainly due to technical difficulties associated with approximating\nposterior distributions and utility functions. We devise a novel experimental\ndesign framework for implicit models that improves upon previous work in two\nways. First, we use the mutual information between parameters and data as the\nutility function, which has previously not been feasible. We achieve this by\nutilising Likelihood-Free Inference by Ratio Estimation (LFIRE) to approximate\nposterior distributions, instead of the traditional approximate Bayesian\ncomputation or synthetic likelihood methods. Secondly, we use Bayesian\noptimisation in order to solve the optimal design problem, as opposed to the\ntypically used grid search or sampling-based methods. We find that this\nincreases efficiency and allows us to consider higher design dimensions.","url_abs":"http://arxiv.org/abs/1810.09912v2","url_pdf":"http://arxiv.org/pdf/1810.09912v2.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":"efficient-bayesian-experimental-design-for","repo_url":"https://github.com/stevenkleinegesse/bedimplicit","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-optimisation","task_name":"Bayesian Optimisation"},{"task_slug":"experimental-design","task_name":"Experimental Design"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.09912","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.09912"}},"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/stevenkleinegesse/bedimplicit","reach":null}],"summary":{"ran_draft_wrong":1,"unverified":1},"by_repo_kind":{"listed":{"samples":2,"ran":1,"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":2,"samples":[{"code_sha256_prefix":"19ace10c9507b315","entry":"fun_dfun","repo":"stevenkleinegesse/bedimplicit","repo_kind":"listed","path":"methods.py","file_url":"https://github.com/stevenkleinegesse/bedimplicit/blob/HEAD/methods.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"19ace10c9507b315"}},{"code_sha256_prefix":"28b00301ca8f8985","entry":"indicator_boundaries","repo":"stevenkleinegesse/bedimplicit","repo_kind":"listed","path":"methods.py","file_url":"https://github.com/stevenkleinegesse/bedimplicit/blob/HEAD/methods.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"28b00301ca8f8985"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}