Convex Optimal Uncertainty Quantification [PDF]
SIAM Journal on Optimization, 2015 Optimal uncertainty quantification (OUQ) is a framework for numerical extreme-case analysis of stochastic systems with imperfect knowledge of the underlying probability distribution.
Han, Shuo +4 more
core +6 more sources
Optimal Uncertainty Quantification [PDF]
SIAM Review, 2012 We propose a rigorous framework for Uncertainty Quantification (UQ) in which the UQ objectives and the assumptions/information set are brought to the forefront.
Bentkus V. +18 more
core +5 more sources
Turbid but accurate: automating lysostaphin quantification including uncertainty quantification [PDF]
Microbial Cell FactoriesConventional methods for measuring antibacterial activity, such as disk-diffusion assays, have limitations in quantitative reliability and require long incubation times making them unsuitable for high-throughput applications. To address these limitations,
Lisa Prigolovkin +8 more
doaj +2 more sources
Uncertainty quantification (UQ) [PDF]
, 2011 This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece.
3rd Micro and Nano Flows Conference (MNF2011) +1 more
core +2 more sources
Uncertainty quantification and Heston model [PDF]
Journal of Mathematics in Industry, 2018 In this paper, we study the impact of the parameters involved in Heston model by means of Uncertainty Quantification. The Stochastic Collocation Method already used for example in computational fluid dynamics, has been applied throughout this work in ...
María Suárez-Taboada +3 more
doaj +7 more sources
Uncertainty Quantification Under Group Sparsity [PDF]
Biometrika, 2017 Quantifying the uncertainty in penalized regression under group sparsity is an important open question. We establish, under a high-dimensional scaling, the asymptotic validity of a modified parametric bootstrap method for the group lasso, assuming a ...
Min, Seunghyun, Zhou, Qing
core +2 more sources
Benchmarking uncertainty quantification for protein engineering. [PDF]
PLoS Computational BiologyMachine learning sequence-function models for proteins could enable significant advances in protein engineering, especially when paired with state-of-the-art methods to select new sequences for property optimization and/or model improvement. Such methods
Kevin P Greenman +2 more
doaj +2 more sources
Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models [PDF]
Trans. Mach. Learn. Res., 2023 Large language models (LLMs) specializing in natural language generation (NLG) have recently started exhibiting promising capabilities across a variety of domains.
Zhen Lin, Shubhendu Trivedi, Jimeng Sun
semanticscholar +1 more source
How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model [PDF]
Geoscientific Model Development, 2023 Pesticide transfers in agricultural catchments are responsible for diffuse but major risks to water quality. Spatialized pesticide transfer models are useful tools to assess the impact of the structure of the landscape on water quality.
E. Rouzies +3 more
doaj +1 more source
Research on application method of uncertainty quantification technology in equipment test identification [PDF]
MATEC Web of Conferences, 2021 This paper introduces the concepts of equipment test qualification and uncertainty quantification, and the analysis framework and process of equipment test uncertainty quantification.
Wang Jiajia +3 more
doaj +1 more source