{"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/bayesian-adaptive-calibration-and-optimal","title":"Bayesian Adaptive Calibration and Optimal Design","arxiv_id":"2405.14440","date":"2024-05-23","proceeding":null,"authors":["Rafael Oliveira","Dino Sejdinovic","David Howard","Edwin V. Bonilla"],"abstract":"The process of calibrating computer models of natural phenomena is essential for applications in the physical sciences, where plenty of domain knowledge can be embedded into simulations and then calibrated against real observations. Current machine learning approaches, however, mostly rely on rerunning simulations over a fixed set of designs available in the observed data, potentially neglecting informative correlations across the design space and requiring a large amount of simulations. Instead, we consider the calibration process from the perspective of Bayesian adaptive experimental design and propose a data-efficient algorithm to run maximally informative simulations within a batch-sequential process. At each round, the algorithm jointly estimates the parameters of the posterior distribution and optimal designs by maximising a variational lower bound of the expected information gain. The simulator is modelled as a sample from a Gaussian process, which allows us to correlate simulations and observed data with the unknown calibration parameters. We show the benefits of our method when compared to related approaches across synthetic and real-data problems.","url_abs":"https://arxiv.org/abs/2405.14440v3","url_pdf":"https://arxiv.org/pdf/2405.14440v3.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":"bayesian-adaptive-calibration-and-optimal","repo_url":"https://github.com/csiro-funml/bacon","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"experimental-design","task_name":"Experimental Design"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"},{"method_slug":"normalizing-flows","method_name":"Normalizing Flows"},{"method_slug":"variational-inference","method_name":"Variational Inference"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2405.14440","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.14440"}},"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/csiro-funml/bacon","reach":null}],"summary":{"ran":2,"ran_draft_wrong":1,"unverified":4},"by_repo_kind":{"official":{"samples":7,"ran":3,"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":"dad6bfdb23eb369b","entry":"BiFiIndexKernel","repo":"csiro-funml/bacon","repo_kind":"official","path":"baed4cal/models/joint_model.py","file_url":"https://github.com/csiro-funml/bacon/blob/HEAD/baed4cal/models/joint_model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"dad6bfdb23eb369b"}},{"code_sha256_prefix":"9fbea687d22e08af","entry":"ConstantKernel","repo":"csiro-funml/bacon","repo_kind":"official","path":"baed4cal/models/joint_model.py","file_url":"https://github.com/csiro-funml/bacon/blob/HEAD/baed4cal/models/joint_model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9fbea687d22e08af"}},{"code_sha256_prefix":"794cfc9c68f8de5d","entry":"get_matern_kernel_with_gamma_prior","repo":"csiro-funml/bacon","repo_kind":"official","path":"baed4cal/models/joint_model.py","file_url":"https://github.com/csiro-funml/bacon/blob/HEAD/baed4cal/models/joint_model.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"794cfc9c68f8de5d"}},{"code_sha256_prefix":"ba951e9e070bcf29","entry":"BiFiKernel","repo":"csiro-funml/bacon","repo_kind":"official","path":"baed4cal/models/joint_model.py","file_url":"https://github.com/csiro-funml/bacon/blob/HEAD/baed4cal/models/joint_model.py","link_basis":"first_harvest_node","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":"ba951e9e070bcf29"}},{"code_sha256_prefix":"a3c468a04b0d01d4","entry":"BiFiScalingKernel","repo":"csiro-funml/bacon","repo_kind":"official","path":"baed4cal/models/joint_model.py","file_url":"https://github.com/csiro-funml/bacon/blob/HEAD/baed4cal/models/joint_model.py","link_basis":"first_harvest_node","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":"a3c468a04b0d01d4"}},{"code_sha256_prefix":"3a616cf87539ec05","entry":"JointFullGP","repo":"csiro-funml/bacon","repo_kind":"official","path":"baed4cal/models/joint_model.py","file_url":"https://github.com/csiro-funml/bacon/blob/HEAD/baed4cal/models/joint_model.py","link_basis":"first_harvest_node","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":"3a616cf87539ec05"}},{"code_sha256_prefix":"79452e04e96f390c","entry":"JointModel","repo":"csiro-funml/bacon","repo_kind":"official","path":"baed4cal/models/joint_model.py","file_url":"https://github.com/csiro-funml/bacon/blob/HEAD/baed4cal/models/joint_model.py","link_basis":"first_harvest_node","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":"79452e04e96f390c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}