102 citations · 129 across the 8 of their papers we have counts for
9 papers
Building Trees for Probabilistic Prediction via Scoring Rules
Sara Shashaani, Ozge Surer, Matthew Plumlee +1
Decision trees built with data remain in widespread use for nonparametric prediction. Predicting probability distributions is preferred over point predictions when uncertainty play…
Sequential Bayesian experimental design for calibration of expensive simulation models
Özge Sürer, Matthew Plumlee, Stefan M. Wild
Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator o…
Constructing a simulation surrogate with partially observed output
Moses Y-H. Chan, Matthew Plumlee, Stefan M. Wild
Gaussian process surrogates are a popular alternative to directly using computationally expensive simulation models. When the simulation output consists of many responses, dimensio…
Bayesian calibration of viscous anisotropic hydrodynamic simulations of heavy-ion collisions
Dananjaya Liyanage, Özge Sürer, Matthew Plumlee +2
Due to large pressure gradients at early times, standard hydrodynamic model simulations of relativistic heavy-ion collisions do not become reliable until \,fm/ after the c…
Get on the BAND Wagon: A Bayesian Framework for Quantifying Model Uncertainties in Nuclear Dynamics
D. R. Phillips, R. J. Furnstahl, U. Heinz +8
We describe the Bayesian Analysis of Nuclear Dynamics (BAND) framework, a cyberinfrastructure that we are developing which will unify the treatment of nuclear models, experimental…
Multi-Resolution Functional ANOVA for Large-Scale, Many-Input Computer Experiments
Chih-Li Sung, Wenjia Wang, Matthew Plumlee +1
The Gaussian process is a standard tool for building emulators for both deterministic and stochastic computer experiments. However, application of Gaussian process models is greatl…
An Uncertainty Quantification Method for Inexact Simulation Models
Matthew Plumlee, Henry Lam
The vast majority of stochastic simulation models are imperfect in that they fail to exactly emulate real system dynamics. The inexactness of the simulation model, or model discrep…
Orthogonal Gaussian process models
Matthew Plumlee, V. Roshan Joseph
Gaussian processes models are widely adopted for nonparameteric/semi-parametric modeling. Identifiability issues occur when the mean model contains polynomials with unknown coeffic…
Fast prediction of deterministic functions using sparse grid experimental designs
Matthew Plumlee
Random field models have been widely employed to develop a predictor of an expensive function based on observations from an experiment. The traditional framework for developing a p…