• Publications
  • Influence
Bayesian Treed Gaussian Process Models With an Application to Computer Modeling
TLDR
This article explores nonstationary modeling methodologies that couple stationary Gaussian processes with treed partitioning, based on the idea of Bayesian partitioning. Expand
  • 504
  • 48
  • PDF
Cases for the nugget in modeling computer experiments
TLDR
We show that estimating a (non-zero) nugget can lead to surrogate models with better statistical properties, such as predictive accuracy and coverage, in a variety of common situations. Expand
  • 186
  • 27
  • PDF
Local Gaussian Process Approximation for Large Computer Experiments
TLDR
We provide a new approach to approximate emulation of large computer experiments. Expand
  • 219
  • 22
  • PDF
Bayesian treed gaussian process models
TLDR
A nonparametric and semiparametric nonstationary modeling methodologies for addressing this need that couples stationary Gaussian processes and (limiting) linear models with treed partitioning. Expand
  • 105
  • 14
  • PDF
Optimization Under Unknown Constraints
TLDR
We develop a statistical approach based on Gaussian processes and Bayesian learning to both approximate the unknown function and estimate the probability of meeting the constraints. Expand
  • 96
  • 13
  • PDF
Practical Heteroscedastic Gaussian Process Modeling for Large Simulation Experiments
TLDR
We show how multiple applications of a well-known Woodbury identity facilitate inference for all parameters under the likelihood (without approximation), bypassing the typical full data sized calculations. Expand
  • 80
  • 13
  • PDF
Adaptive Design and Analysis of Supercomputer Experiments
TLDR
We develop an adaptive sequential design framework to cope with an asynchronous, random, agent–based supercomputing environment by using a hybrid approach that melds optimal strategies from the statistics literature with flexible strategies from active learning literature. Expand
  • 168
  • 12
  • PDF
Regression-Based Earnings Forecasts
We provide a comprehensive examination of regression-based earnings forecasts. Specifically, we evaluate forecasts of scaled and unscaled net income along a number of relevant dimensions includingExpand
  • 46
  • 10
tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models
TLDR
The tgp package for R is a tool for fully Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes with jumps to the limiting linear model. Expand
  • 164
  • 9
  • PDF
Dynamic Trees for Learning and Design
TLDR
We create a sequential tree model whose state changes in time with the accumulation of new data, and provide particle learning algorithms that allow for the efficient online posterior filtering of tree states. Expand
  • 101
  • 9
  • PDF
...
1
2
3
4
5
...