Uncertainty Quantification for Computer Models With Spatial Output Using Calibration-Optimal Bases
From MaRDI portal
Publication:5208085
DOI10.1080/01621459.2018.1514306zbMath1428.62117arXiv1801.08184OpenAlexW3102718757MaRDI QIDQ5208085
Daniel B. Williamson, John Scinocca, Viatcheslav Kharin, James M. Salter
Publication date: 15 January 2020
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1801.08184
Directional data; spatial statistics (62H11) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15)
Related Items (9)
A higher-order singular value decomposition tensor emulator for spatiotemporal simulators ⋮ Ice model calibration using semicontinuous spatial data ⋮ Quantifying Spatio-Temporal Boundary Condition Uncertainty for the North American Deglaciation ⋮ Deep Gaussian processes for calibration of computer models (with discussion) ⋮ A hierarchical spatiotemporal statistical model motivated by glaciology ⋮ A theoretical framework of the scaled Gaussian stochastic process in prediction and calibration ⋮ Diagnostics-Driven Nonstationary Emulators Using Kernel Mixtures ⋮ Analyzing stochastic computer models: a review with opportunities ⋮ Computer Model Calibration with Time Series Data Using Deep Learning and Quantile Regression
Uses Software
Cites Work
- Unnamed Item
- Efficient emulators of computer experiments using compactly supported correlation functions, with an application to cosmology
- Reified Bayesian modelling and inference for physical systems
- Bayesian emulation of complex multi-output and dynamic computer models
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- Analysis methods for computer experiments: how to assess and what counts?
- Principal component analysis.
- Parallel partial Gaussian process emulation for computer models with massive output
- Computer model validation with functional output
- Galaxy formation: a Bayesian uncertainty analysis
- Bayesian Calibration of Computer Models
- Learning about physical parameters: the importance of model discrepancy
- Computer Model Calibration Using High-Dimensional Output
- Exploratory ensemble designs for environmental models using k‐extended Latin Hypercubes
- A comparison of statistical emulation methodologies for multi‐wave calibration of environmental models
This page was built for publication: Uncertainty Quantification for Computer Models With Spatial Output Using Calibration-Optimal Bases
