1.6k citations · 1.7k across the 16 of their papers we have counts for
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MLCBART: Multilabel Classification with Bayesian Additive Regression Trees
Jiahao Tian, Hugh Chipman, Thomas Loughin
Multilabel Classification (MLC) deals with the simultaneous classification of multiple binary labels. The task is challenging because, not only may there be arbitrarily different a…
Let's practice what we preach: Planning and interpreting simulation studies with design and analysis of experiments
Hugh Chipman, Derek Bingham
Statisticians recommend the Design and Analysis of Experiments (DAE) for evidence-based research but often use tables to present their own simulation studies. Could DAE do better?…
Heteroscedastic BART Using Multiplicative Regression Trees
Matthew Pratola, Hugh Chipman, Edward George +1
BART (Bayesian Additive Regression Trees) has become increasingly popular as a flexible and scalable nonparametric regression approach for modern applied statistics problems. For t…
Monotone Function Estimation for Computer Experiments
Shirin Golchi, Derek R. Bingham, Hugh Chipman +1
In statistical modeling of computer experiments sometimes prior information is available about the underlying function. For example, the physical system simulated by the computer c…
A new Bayesian ensemble of trees classifier for identifying multi-class labels in satellite images
Reshu Agarwal, Pritam Ranjan, Hugh Chipman
Classification of satellite images is a key component of many remote sensing applications. One of the most important products of a raw satellite image is the classified map which l…
Sequential Design for Computer Experiments with a Flexible Bayesian Additive Model
Hugh Chipman, Pritam Ranjan, Weiwei Wang
In computer experiments, a mathematical model implemented on a computer is used to represent complex physical phenomena. These models, known as computer simulators, enable experime…