Longitudinal data analysis is an essential statistical approach for studying phenomena observed repeatedly over time, allowing researchers to explore both within-subject and between-subject variations ...

Motivated by examples from genetic association studies, this article considers the model selection problem in a general complex linear model system and in a Bayesian framework. We discuss formulating ...

We apply a linear Bayesian model to seismic tomography, a high-dimensional inverse problem in geophysics. The objective is to estimate the three-dimensional structure of the earth's interior from data ...

Dr. James McCaffrey of Microsoft Research says the main advantage of using Gaussian naive Bayes classification compared to other techniques like decision trees or neural networks is that you don't ...

Dr. Wang, who also serves as the founding director for research in the Division of Data Science, is leading efforts to create ...

A novel Bayesian Hierarchical Network Model (BHNM) is designed for ensemble predictions of daily river stage, leveraging the spatial interdependence of river networks and hydrometeorological variables ...

Oil spills can be among the most devastating environmental disasters, with the potential to severely damage marine ecosystems, disrupt coastal communities, and impose lasting economic damage.