Bayesian Dependent Mixture Models: A Predictive Comparison and Survey Page 1
Statistical Science 2025, Vol. 40, No. 1, 81–108 https://doi.org/10.1214/24-STS966 © …

Electrochemical impedance spectroscopy (EIS) is widely used to study the properties of
electrochemical materials and systems. However, analyzing EIS data remains challenging …

There has been a long-standing challenge in developing locally stationary Gaussian
process models concerning how to obtain flexible partitions and make predictions near …

This paper presents a Gaussian process (GP) model for estimating piecewise continuous
regression functions. In many scientific and engineering applications of regression analysis …

Active learning is a subfield of machine learning that focuses on improving the data
collection efficiency in expensive-to-evaluate systems. Active learning-applied surrogate …

We introduce a zero-censored Gaussian process as a systematic, model-based approach to
building Gaussian process emulators for range-constrained simulator output. This approach …

Abstract The Bayesian generative kernel Gaussian process regression (BGKGPR), a novel
progressive probabilistic approach for nonparametric modeling with an optimal generative …

Abstract The Additive Voronoi Tessellations (AddiVortes) model is a multivariate regression
model that uses Voronoi tessellations to partition the covariate space in an additive …

Large uncertainties persist in modeling shallow, low clouds because of many interacting
nonlinear processes and multiple cloud‐controlling environmental factors. In addition, sharp …

Active learning of Gaussian process (GP) surrogates has been useful for optimizing
experimental designs for physical/computer simulation experiments, and for steering data …