10 citations · 12 across the 4 of their papers we have counts for
9 papers
Fixed-domain Posterior Contraction Rates for Spatial Gaussian Process Model with Nugget
Cheng Li, Saifei Sun, Yichen Zhu
Spatial Gaussian process regression models typically contain finite dimensional covariance parameters that need to be estimated from the data. We study the Bayesian estimation of c…
Bayesian Fixed-domain Asymptotics for Covariance Parameters in a Gaussian Process Model
Cheng Li
Gaussian process models typically contain finite dimensional parameters in the covariance function that need to be estimated from the data. We study the Bayesian fixed-domain asymp…
Distributed Bayesian Varying Coefficient Modeling Using a Gaussian Process Prior
Rajarshi Guhaniyogi, Cheng Li, Terrance D. Savitsky +1
Varying coefficient models (VCMs) are widely used for estimating nonlinear regression functions for functional data. Their Bayesian variants using Gaussian process priors on the fu…
Classification Trees for Imbalanced and Sparse Data: Surface-to-Volume Regularization
Yichen Zhu, Cheng Li, David B. Dunson
Classification algorithms face difficulties when one or more classes have limited training data. We are particularly interested in classification trees, due to their interpretabili…
A Divide-and-Conquer Bayesian Approach to Large-Scale Kriging
Rajarshi Guhaniyogi, Cheng Li, Terrance D. Savitsky +1
We propose a three-step divide-and-conquer strategy within the Bayesian paradigm that delivers massive scalability for any spatial process model. We partition the data into a large…
Simple, Scalable and Accurate Posterior Interval Estimation
Cheng Li, Sanvesh Srivastava, David B. Dunson
There is a lack of simple and scalable algorithms for uncertainty quantification. Bayesian methods quantify uncertainty through posterior and predictive distributions, but it is di…