1 citations · 1 across the 2 of their papers we have counts for
9 papers
A Kernel-Based Approach for Modelling Gaussian Processes with Functional Information
D. Andrew Brown, Peter Kiessler, John Nicholson
Gaussian processes (GPs) are ubiquitous tools for modeling and predicting continuous processes in physical and engineering sciences. This is partly due to the fact that one may emp…
Bayesian Regularization for Functional Graphical Models with Applications to Neuroimaging
Jiajing Niu, Boyoung Hur, John Absher +1
Graphical models, used to express conditional dependence between random variables observed at various nodes, are used extensively in many fields such as genetics, neuroscience, and…
Coupling material and mechanical design processes via computer model calibration
Carl Ehrett, D. Andrew Brown, Evan Chodora +2
Computer model calibration typically operates by choosing parameter values in a computer model so that the model output faithfully predicts reality. By using performance targets in…
Efficient Marginalization-based MCMC Methods for Hierarchical Bayesian Inverse Problems
Arvind K. Saibaba, Johnathan Bardsley, D. Andrew Brown +1
Hierarchical models in Bayesian inverse problems are characterized by an assumed prior probability distribution for the unknown state and measurement error precision, and hyper-pri…
Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging
D. Andrew Brown, Christopher S. McMahan, Russell T. Shinohara +1
Alzheimer's disease is a neurodegenerative condition that accelerates cognitive decline relative to normal aging. It is of critical scientific importance to gain a better understan…
Sampling strategies for fast updating of Gaussian Markov random fields
D. Andrew Brown, Christopher S. McMahan, Stella Watson Self
Gaussian Markov random fields (GMRFs) are popular for modeling dependence in large areal datasets due to their ease of interpretation and computational convenience afforded by the…