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 ...

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 ...

This paper extends the Bayesian Model Averaging framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited ...

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 ...

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 ...

A study published in JCO Clinical Cancer Informatics demonstrates that machine learning models incorporating patient-reported ...