17 citations · 38 across the 9 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…
Low Rank Independence Samplers in Bayesian Inverse Problems
D. Andrew Brown, Arvind Saibaba, Sarah Vallélian
In Bayesian inverse problems, the posterior distribution is used to quantify uncertainty about the reconstructed solution. In practice, Markov chain Monte Carlo algorithms often ar…
Nonparametric Functional Calibration of Computer Models
D. Andrew Brown, Sez Atamturktur
Standard methods in computer model calibration treat the calibration parameters as constant throughout the domain of control inputs. In many applications, systematic variation may…
A Bayesian Generalized CAR Model for Correlated Signal Detection
D. Andrew Brown, Gauri S. Datta, Nicole A. Lazar
Over the last decade, large-scale multiple testing has found itself at the forefront of modern data analysis. In many applications data are correlated, so that the observed test st…