Active learning for efficiently training emulators of computationally expensive mathematical models
نویسندگان
چکیده
منابع مشابه
Uncertainty Analysis for Computationally Expensive Models with Multiple Outputs
Bayesian MCMC calibration and uncertainty analysis for computationally expensive models is implemented using the SOARS (Statistical and Optimization Analysis using Response Surfaces) methodology. SOARS uses a radial basis function interpolator as a surrogate, also known as an emulator or meta-model, for the logarithm of the posterior density. To prevent wasteful evaluations of the expensive mod...
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ژورنال
عنوان ژورنال: Statistics in Medicine
سال: 2020
ISSN: 0277-6715,1097-0258
DOI: 10.1002/sim.8679