30 citations · 170 across the 22 of their papers we have counts for
9 papers · 1 filter
GNet: A scalable and flexible Gaussian process network with nonparametric neurons
Mengyang Gu
We build GNet, a scalable and flexible Gaussian process network with nonparametric activation functions. The key computational contribution is to make estimation of GP activations…
Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification
Mengyang Gu, Yizi Lin, Victor Chang Lee +1
Data-driven modeling is useful for reconstructing nonlinear dynamical systems when the underlying process is unknown or too expensive to compute. Having reliable uncertainty assess…
A Nonparametric Mixed-Effects Mixture Model for Patterns of Clinical Measurements Associated with COVID-19
Xiaoran Ma, Wensheng Guo, Mengyang Gu +3
Some patients with COVID-19 show changes in signs and symptoms such as temperature and oxygen saturation days before being positively tested for SARS-CoV-2, while others remain asy…
Gaussian orthogonal latent factor processes for large incomplete matrices of correlated data
Mengyang Gu, Hanmo Li
We introduce Gaussian orthogonal latent factor processes for modeling and predicting large correlated data. To handle the computational challenge, we first decompose the likelihood…
Calibration of imperfect geophysical models by multiple satellite interferograms with measurement bias
Mengyang Gu, Kyle Anderson, Erika McPhillips
Model calibration consists of using experimental or field data to estimate the unknown parameters of a mathematical model. The presence of model discrepancy and measurement bias in…
Generalized probabilistic principal component analysis of correlated data
Mengyang Gu, Weining Shen
Principal component analysis (PCA) is a well-established tool in machine learning and data processing. The principal axes in PCA were shown to be equivalent to the maximum marginal…
Jointly Robust Prior for Gaussian Stochastic Process in Emulation, Calibration and Variable Selection
Mengyang Gu
Gaussian stochastic process (GaSP) has been widely used in two fundamental problems in uncertainty quantification, namely the emulation and calibration of mathematical models. Some…
Fast Nonseparable Gaussian Stochastic Process with Application to Methylation Level Interpolation
Mengyang Gu, Yanxun Xu
Gaussian stochastic process (GaSP) has been widely used as a prior over functions due to its flexibility and tractability in modeling. However, the computational cost in evaluating…
Scaled Gaussian Stochastic Process for Computer Model Calibration and Prediction
Mengyang Gu, Long Wang
We consider the problem of calibrating an imperfect computer model using experimental data. To compensate the misspecification of the computer model and make more accurate predicti…