6 papers
Scalable Heteroskedastic Gaussian Process Models for Large Inhomogeneous Datasets
K. Potter, K. R. Moran, R. Ulrich +5
We introduce Heteroskedastic Normalized Vecchia Gaussian Processes (HetNV), a scalable framework for Gaussian process regression with input-dependent observation noise. HetNV combi…
Nonparametric Bayesian Calibration of Computer Models
Haiyi Shi, Lei Yang, Jiarui Chi +4
Combining field data and computer models is a crucial step for making inferences, predictions, and decisions for complex science and engineering systems. We formulate and analyze a…
Deep Gaussian Process Emulation and Uncertainty Quantification for Large Computer Experiments
Faezeh Yazdi, Derek Bingham, Daniel Williamson
Computer models are used as a way to explore complex physical systems. Stationary Gaussian process emulators, with their accompanying uncertainty quantification, are popular surrog…
Enhancing Approximate Modular Bayesian Inference by Emulating the Conditional Posterior
Grant Hutchings, Kellin Rumsey, Derek Bingham +1
In modular Bayesian analyses, complex models are composed of distinct modules, each representing different aspects of the data or prior information. In this context, fully Bayesian…
Fast Emulation, Modular Calibration, and Active Learning for Simulators with Functional Response
Grant Hutchings, Derek Bingham, Kellin Rumsey +1
Scalable surrogate models enable efficient emulation of computer models (or simulators), particularly when dealing with large ensembles of runs. While Gaussian process (GP) models…
Uncertainty Quantification of a Computer Model for Binary Black Hole Formation
Luyao Lin, Derek Bingham, Floor Broekgaarden +1
In this paper, a fast and parallelizable method based on Gaussian Processes (GPs) is introduced to emulate computer models that simulate the formation of binary black holes (BBHs)…