Quantifying simulator discrepancy in discrete-time dynamical simulators

DOI10.1007/S13253-011-0077-3zbMath1306.62361OpenAlexW2065176297MaRDI QIDQ2261045

Jeremy E. Oakley, Richard D. Wilkinson, Dan Cornford, Michail D. Vrettas

Publication date: 6 March 2015

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://publications.aston.ac.uk/id/eprint/16147/1/Wilkinson_etal_Shortened.pdf

zbMATH Keywords

model errorMonte Carlo EM algorithmrainfall-runoff model

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12)

Related Items (2)

Mixtures of experts for understanding model discrepancy in dynamic computer models ⋮ Considering discrepancy when calibrating a mechanistic electrophysiology model

Cites Work

• Unnamed Item
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• To explain or to predict?
• Reified Bayesian modelling and inference for physical systems
• Galaxy formation: a Bayesian uncertainty analysis
• Sequential Monte Carlo Methods in Practice
• Bayesian Calibration of Computer Models
• Gaussian process emulation of dynamic computer codes
• Computer Model Calibration Using High-Dimensional Output
• Bayesian Modeling of Uncertainty in Ensembles of Climate Models
• Strictly Proper Scoring Rules, Prediction, and Estimation
• Ascent-Based Monte Carlo Expectation– Maximization
• Monte Carlo Smoothing for Nonlinear Time Series
• Adjoint methods in data assimilation for estimating model error

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