169 citations · 341 across the 21 of their papers we have counts for
21 papers
Bayesian model-data comparison incorporating theoretical uncertainties
Sunil Jaiswal, Chun Shen, Richard J. Furnstahl +2
Accurate comparisons between theoretical models and experimental data are critical for scientific progress. However, inferred physical model parameters can vary significantly with…
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…
Assessing Correlated Truncation Errors in Modern Nucleon-Nucleon Potentials
P. J. Millican, R. J. Furnstahl, J. A. Melendez +2
We test the BUQEYE model of correlated effective field theory (EFT) truncation errors on Reinert, Krebs, and Epelbaum's semi-local momentum-space implementation of the chiral EFT (…
Sharded Bayesian Additive Regression Trees
Hengrui Luo, Matthew T. Pratola
In this paper we develop the randomized Sharded Bayesian Additive Regression Trees (SBT) model. We introduce a randomization auxiliary variable and a sharding tree to decide partit…
Estimating Shapley Effects in Big-Data Emulation and Regression Settings using Bayesian Additive Regression Trees
Akira Horiguchi, Matthew T. Pratola
Shapley effects are a particularly interpretable approach to assessing how a function depends on its various inputs. The existing literature contains various estimators for this cl…
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…