Assessing the impacts of parameter uncertainty for computationally expensive groundwater 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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The generalized likelihood uncertainty estimation (GLUE) framework has been widely used in hydrologic studies. However, the extensive random sampling causes a high computational burden that prohibits the efficient application of GLUE to costly distributed hydrologic models such as the soil and water assessment tool (SWAT). In this study, a multimodal optimization algorithm called isolatedspecia...
متن کاملthe application of multivariate probit models for conditional claim-types (the case study of iranian car insurance industry)
هدف اصلی نرخ گذاری بیمه ای تعیین نرخ عادلانه و منطقی از دیدگاه بیمه گر و بیمه گذار است. تعین نرخ یکی از مهم ترین مسایلی است که شرکتهای بیمه با آن روبرو هستند، زیرا تعیین نرخ اصلی ترین عامل در رقابت بین شرکتها است. برای تعیین حق بیمه ابتدا می باید مقدار مورد انتظار ادعای خسارت برای هر قرارداد بیمه را برآورد کرد. روش عمومی مدل سازی خسارتهای عملیاتی در نظر گرفتن تواتر و شدت خسارتها می باشد. اگر شر...
15 صفحه اولRegarding probabilistic analysis and computationally expensive models: necessary and required?
OBJECTIVE To assess the importance of considering decision uncertainty, the appropriateness of probabilistic sensitivity analysis (PSA), and the use of patient-level simulation (PLS) in appraisals for the National Institute for Health and Clinical Excellence (NICE). METHODS Decision-makers require estimates of decision uncertainty alongside expected net benefits (NB) of interventions. This re...
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Probabilistic inversion is used to take expert uncertainty assessments about observable model outputs and build from them a distribution on the model parameters that captures the uncertainty expressed by the experts. In this paper we look at ways to use minimum information methods to do this, focussing in particular on the problem of ensuring consistency between expert assessments about differi...
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ژورنال
عنوان ژورنال: Water Resources Research
سال: 2006
ISSN: 0043-1397
DOI: 10.1029/2005wr004640