1 citations · 3 across the 6 of their papers we have counts for
6 papers
Combining Climate Models using Bayesian Regression Trees and Random Paths
John C. Yannotty, Thomas J. Santner, Bo Li +1
General circulation models (GCMs) are essential tools for climate studies. Such climate models may have varying accuracy across the input domain, but no model is uniformly best. On…
Model Mixing Using Bayesian Additive Regression Trees
John C. Yannotty, Thomas J. Santner, Richard J. Furnstahl +1
In modern computer experiment applications, one often encounters the situation where various models of a physical system are considered, each implemented as a simulator on a comput…
Using BART to Perform Pareto Optimization and Quantify its Uncertainties
Akira Horiguchi, Thomas J. Santner, Ying Sun +1
Techniques to reduce the energy burden of an industrial ecosystem often require solving a multiobjective optimization problem. However, collecting experimental data can often be ei…
Assessing variable activity for Bayesian regression trees
Akira Horiguchi, Matthew T. Pratola, Thomas J. Santner
Bayesian Additive Regression Trees (BART) are non-parametric models that can capture complex exogenous variable effects. In any regression problem, it is often of interest to learn…
Sequential Design of Mixture Experiments with an Empirically Determined Input Domain and an Application to Burn-up Credit Penalization of Nuclear Fuel Rods
François Bachoc, Théo Barthe, Thomas Santner +1
This paper proposes a sequential design for maximizing a stochastic computer simulator output, y(x), over an unknown optimization domain. The training data used to estimate the opt…
Prediction Using a Bayesian Heteroscedastic Composite Gaussian Process
Casey B. Davis, Christopher M. Hans, Thomas J. Santner
This research proposes a flexible Bayesian extension of the composite Gaussian process (CGP) model of Ba and Joseph (2012) for predicting (stationary or) non-stationary $y(\mathbf{…