6 citations · 6 across the 5 of their papers we have counts for
5 papers
Bayesian Emulation of Multi-fidelity Earth System Modelling Using Hierarchical Gaussian Processes
Xiaoyu Xiong, Louise Kimpton, Huiyi Yang +3
Multi-fidelity Earth system models provide simulations at different levels of complexity and computational cost, but exhaustive exploration of the parameter space at the highest fi…
Bayesian Hierarchical Emulators for Multi-Level Models: BayHEm
Louise Kimpton, James Salter, Xiaoyu Xiong +1
Decision making often uses complex computer codes run at the exa-scale (10e18 flops). Such computer codes or models are often run in a hierarchy of different levels of fidelity ran…
Uncertainty Quantification for Agent Based Models: A Tutorial
Louise Kimpton, Peter Challenor, James Salter
We explore the application of uncertainty quantification methods to agent-based models (ABMs) using a simple sheep and wolf predator-prey model. This work serves as a tutorial on h…
Propagating moments in probabilistic graphical models with polynomial regression forms for decision support systems
Victoria Volodina, Nikki Sonenberg, Peter Challenor +1
Probabilistic graphical models are widely used to model complex systems under uncertainty. Traditionally, Gaussian directed graphical models are applied for analysis of large netwo…
Comparison of surrogate-based uncertainty quantification methods for computationally expensive simulators
N. E. Owen, P. Challenor, P. P. Menon +1
Polynomial chaos and Gaussian process emulation are methods for surrogate-based uncertainty quantification, and have been developed independently in their respective communities ov…