{"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/gpdoemd-a-python-package-for-design-of","title":"GPdoemd: a Python package for design of experiments for model discrimination","arxiv_id":"1810.02561","date":"2018-10-05","proceeding":null,"authors":["Simon Olofsson","Lukas Hebing","Sebastian Niedenführ","Marc Peter Deisenroth","Ruth Misener"],"abstract":"Model discrimination identifies a mathematical model that usefully explains\nand predicts a given system's behaviour. Researchers will often have several\nmodels, i.e. hypotheses, about an underlying system mechanism, but insufficient\nexperimental data to discriminate between the models, i.e. discard inaccurate\nmodels. Given rival mathematical models and an initial experimental data set,\noptimal design of experiments suggests maximally informative experimental\nobservations that maximise a design criterion weighted by prediction\nuncertainty. The model uncertainty requires gradients, which may not be readily\navailable for black-box models. This paper (i) proposes a new design criterion\nusing the Jensen-R\\'enyi divergence, and (ii) develops a novel method replacing\nblack-box models with Gaussian process surrogates. Using the surrogates, we\nmarginalise out the model parameters with approximate inference. Results show\nthese contributions working well for both classical and new test instances. We\nalso (iii) introduce and discuss GPdoemd, the open-source implementation of the\nGaussian process surrogate method.","url_abs":"http://arxiv.org/abs/1810.02561v3","url_pdf":"http://arxiv.org/pdf/1810.02561v3.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":"gpdoemd-a-python-package-for-design-of","repo_url":"https://github.com/cog-imperial/GPdoemd","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.02561","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.02561"}},"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. 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