Zobrazeno 1 - 10
of 157
pro vyhledávání: '"Higdon, Dave"'
Autor:
Holthuijzen, Maike F., Gramacy, Robert B., Carey, Cayelan C., Higdon, Dave M., Thomas, R. Quinn
We present a novel forecasting framework for lake water temperature profiles, crucial for managing lake ecosystems and drinking water resources. The General Lake Model (GLM), a one-dimensional process-based model, has been widely used for this purpos
Externí odkaz:
http://arxiv.org/abs/2407.03312
In this chapter, we address the challenge of exploring the posterior distributions of Bayesian inverse problems with computationally intensive forward models. We consider various multivariate proposal distributions, and compare them with single-site
Externí odkaz:
http://arxiv.org/abs/2405.00397
Publikováno v:
Bayesian statistics 6 (1), 761-768, 1999
Standard geostatistical models assume stationarity and rely on a variogram model to account for the spatial dependence in the observed data. In some instances, this assumption that the spatial dependence structure is constant throughout the sampling
Externí odkaz:
http://arxiv.org/abs/2212.08043
Tropical cyclones present a serious threat to many coastal communities around the world. Many numerical weather prediction models provide deterministic forecasts with limited measures of their forecast uncertainty. Standard postprocessing techniques
Externí odkaz:
http://arxiv.org/abs/2210.16683
Akademický článek
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Autor:
Fadikar, Arindam, Higdon, Dave, Chen, Jiangzhuo, Lewis, Brian, Venkatramanan, Srini, Marathe, Madhav
In a number of cases, the Quantile Gaussian Process (QGP) has proven effective in emulating stochastic, univariate computer model output (Plumlee and Tuo, 2014). In this paper, we develop an approach that uses this emulation approach within a Bayesia
Externí odkaz:
http://arxiv.org/abs/1712.00546
Autor:
Holthuijzen, Maike F., Beckage, Brian, Clemins, Patrick J., Higdon, Dave, Winter, Jonathan M.
Publikováno v:
Journal of Applied Meteorology and Climatology, 2021 Apr 01. 60(4), 455-475.
Externí odkaz:
https://www.jstor.org/stable/27071753
Publikováno v:
J. Phys. G: Nucl. Part. Phys. 42, 034009 (2015)
Bayesian methods have been very successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based model $\eta(\theta)$ where
Externí odkaz:
http://arxiv.org/abs/1407.3017
Publikováno v:
J. Phys. G: Nucl. Part. Phys. 42, 034024 (2015)
Nuclear density functional theory (DFT) is the only microscopic, global approach to the structure of atomic nuclei. It is used in numerous applications, from determining the limits of stability to gaining a deep understanding of the formation of elem
Externí odkaz:
http://arxiv.org/abs/1406.4383
Autor:
Higdon, Dave, Pratola, Matt, Gattiker, James, Lawrence, Earl, Habib, Salman, Heitmann, Katrin, Price, Steve, Jackson, Charles, Tobis, Michael
The ensemble Kalman filter (EnKF) (Evensen, 2009) has proven effective in quantifying uncertainty in a number of challenging dynamic, state estimation, or data assimilation, problems such as weather forecasting and ocean modeling. In these problems a
Externí odkaz:
http://arxiv.org/abs/1204.3547