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 ...

Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells ...

Selecting a subset of variables for linear models remains an active area of research. This article reviews many of the recent contributions to the Bayesian model selection and shrinkage prior ...

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 ...

Inequality of opportunity has great normative importance. This has led to a literature on measuring the part of overall inequality that is due to circumstances outside of a person’s control. We ...

Linear mixed models are emerging as the method of choice for association testing in genome-wide association studies (GWAS) because they account for both population stratification and cryptic ...

Specialization: Bayesian Statistics for Data Science Instructor: Brian Zaharatos Prior knowledge needed: Statistics View on Coursera Learning Outcomes Articulate the primary interpretations of ...

Bayesian Regression Analysis Generalized linear mixed-effects models with Bayesian estimation were used to investigate the relationship between demographic variables and binarized segmentation quality ...