3 papers
stat.ME2026
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,…
stat.ME2026
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…
stat.ME2025
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…