4 papers
Wasserstein-type Gaussian Process Regressions for Input Measurement Uncertainty
Hengrui Luo, Xiaoye S. Li, Yang Liu +3
Gaussian process (GP) regression is widely used for uncertainty quantification, yet the standard formulation assumes noise-free covariates. When inputs are measured with error, thi…
Calibrating Bayesian Inference
Yang Liu, Jonathan P. Williams, Jan Hannig
Bayesian statistics has gained popularity in psychological research due to its intuitive uncertainty quantification and convenient information-updating rules. In many applications,…
An Improved Solution to the Two Normal Means Problem via Regularization
Yang Liu, Jonathan P. Williams
The many-normal-means problem is a classic example that motivates the development of many important inferential procedures in the history of statistics. In this short note, we cons…
Decision theory via model-free generalized fiducial inference
Jonathan P Williams, Yang Liu
Building on the recent development of the model-free generalized fiducial (MFGF) paradigm (Williams, 2023) for predictive inference with finite-sample frequentist validity guarante…