Deyu Ming
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View article: Integrative Analysis and Imputation of Multiple Data Streams via Deep Gaussian Processes
Integrative Analysis and Imputation of Multiple Data Streams via Deep Gaussian Processes Open
Motivation Healthcare data, particularly in critical care settings, presents three key challenges for analysis. First, physiological measurements come from different sources but are inherently related. Yet, traditional methods often treat …
View article: Distribution of Deep Gaussian process Gradients and Sequential Design for Simulators with Sharp Variations
Distribution of Deep Gaussian process Gradients and Sequential Design for Simulators with Sharp Variations Open
Deep Gaussian Processes (DGPs), multi-layered extensions of GPs, better emulate simulators with regime transitions or sharp changes than standard GPs. Gradient information is crucial for tasks like sensitivity analysis and dimension reduct…
View article: Linked Deep Gaussian Process Emulation for Model Networks
Linked Deep Gaussian Process Emulation for Model Networks Open
Modern scientific problems are often multi-disciplinary and require integration of computer models from different disciplines, each with distinct functional complexities, programming environments, and computation times. Linked Gaussian pro…
View article: Deep Gaussian Process Emulation using Stochastic Imputation
Deep Gaussian Process Emulation using Stochastic Imputation Open
Deep Gaussian processes (DGPs) provide a rich class of models that can better represent functions with varying regimes or sharp changes, compared to conventional GPs. In this work, we propose a novel inference method for DGPs for computer …
View article: Deep Gaussian Process Emulation using Stochastic Imputation
Deep Gaussian Process Emulation using Stochastic Imputation Open
Deep Gaussian processes (DGPs) provide a rich class of models that can better represent functions with varying regimes or sharp changes, compared to conventional GPs. In this work, we propose a novel inference method for DGPs for computer …
View article: Linked Gaussian Process Emulation for Systems of Computer Models Using Matérn Kernels and Adaptive Design
Linked Gaussian Process Emulation for Systems of Computer Models Using Matérn Kernels and Adaptive Design Open
Linked Gaussian Process Emulation for Systems of Computer Models Using Matérn Kernels and Adaptive Design
View article: Notes on Seismic Source Models in Elastostatics
Notes on Seismic Source Models in Elastostatics Open
Seismic source models in elastostatics lay the foundations for the popular earthquake deformation formula derived in Okada (1992), which forms key components for many studies in earthquake displacement simulations, earthquake source invers…
View article: Integrated Emulators for Systems of Computer Models
Integrated Emulators for Systems of Computer Models Open
We generalize the state-of-the-art linked emulator for a system of two computer models under the squared exponential kernel to an integrated emulator for any feed-forward system of multiple computer models, under a variety of kernels (expo…
View article: Linked Gaussian Process Emulation for Systems of Computer Models using Matérn Kernels and Adaptive Design
Linked Gaussian Process Emulation for Systems of Computer Models using Matérn Kernels and Adaptive Design Open
The state-of-the-art linked Gaussian process offers a way to build analytical emulators for systems of computer models. We generalize the closed form expressions for the linked Gaussian process under the squared exponential kernel to a cla…