Extracting falsifiable predictions from sloppy models

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dc.creatorGutenkunst, Ryan N.
dc.creatorCasey, Fergal P.
dc.creatorWaterfall, Joshua J.
dc.creatorMyers, Christopher R.
dc.creatorSethna, James P.
dc.date2007-04-23
dc.date.accessioned2026-07-07T08:44:21Z
dc.date.available2026-07-07T08:44:21Z
dc.descriptionSuccessful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.
dc.description4 pages, 2 figures. Submitted to the Annals of the New York Academy of Sciences for publication in "Reverse Engineering Biological Networks: Opportunities and Challenges in Computational Methods for Pathway Inference"
dc.identifierhttps://arxiv.org/abs/0704.3049
dc.identifierhttp://arxiv.org/abs/0704.3049
dc.identifierAnnals of the New York Academy of Sciences 1115:203-211 (2007)
dc.identifierdoi:10.1196/annals.1407.003
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/142627
dc.subjectQuantitative Methods
dc.titleExtracting falsifiable predictions from sloppy models
dc.typetext

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