184 citations · 815 across the 63 of their papers we have counts for
33 papers · 1 filter
Vecchia approximated Bayesian heteroskedastic Gaussian processes
Parul V. Patil, Robert B. Gramacy, Cayelan C. Carey +1
Many computer simulations are stochastic and exhibit input dependent noise. In such situations, heteroskedastic Gaussian processes (hetGPs) make ideal surrogates as they estimate a…
Modular Jump Gaussian Processes
Anna R. Flowers, Christopher T. Franck, Mickaël Binois +2
Gaussian processes (GPs) furnish accurate nonlinear predictions with well-calibrated uncertainty. However, the typical GP setup has a built-in stationarity assumption, making it il…
Contour Location for Reliability in Airfoil Simulation Experiments using Deep Gaussian Processes
Annie S. Booth, S. Ashwin Renganathan, Robert B. Gramacy
Bayesian deep Gaussian processes (DGPs) outperform ordinary GPs as surrogate models of complex computer experiments when response surface dynamics are non-stationary, which is espe…
Nonstationary Gaussian Process Surrogates
Annie S. Booth, Andrew Cooper, Robert B. Gramacy
We provide a survey of nonstationary surrogate models which utilize Gaussian processes (GPs) or variations thereof, including nonstationary kernel adaptations, partition and local…
Augmenting a simulation campaign for hybrid computer model and field data experiments
Scott Koermer, Justin Loda, Aaron Noble +1
The Kennedy and O'Hagan (KOH) calibration framework uses coupled Gaussian processes (GPs) to meta-model an expensive simulator (first GP), tune its ``knobs" (calibration inputs) to…
Large-scale local surrogate modeling of stochastic simulation experiments
D Austin Cole, Robert B Gramacy, Mike Ludkovski
Gaussian process (GP) surrogate modeling for large computer experiments is limited by cubic runtimes, especially with data from stochastic simulations with input-dependent noise. A…
Entropy-based adaptive design for contour finding and estimating reliability
D. Austin Cole, Robert B. Gramacy, James E. Warner +3
In reliability analysis, methods used to estimate failure probability are often limited by the costs associated with model evaluations. Many of these methods, such as multifidelity…
Multi-output calibration of a honeycomb seal via on-site surrogates
Jiangeng Huang, Robert B. Gramacy
We consider large-scale industrial computer model calibration, combining multi-output simulation with limited physical observation, involved in the development of a honeycomb seal.…
Active Learning for Deep Gaussian Process Surrogates
Annie Sauer, Robert B. Gramacy, David Higdon
Deep Gaussian processes (DGPs) are increasingly popular as predictive models in machine learning (ML) for their non-stationary flexibility and ability to cope with abrupt regime ch…
Locally induced Gaussian processes for large-scale simulation experiments
D. Austin Cole, Ryan Christianson, Robert B. Gramacy
Gaussian processes (GPs) serve as flexible surrogates for complex surfaces, but buckle under the cubic cost of matrix decompositions with big training data sizes. Geospatial and ma…
Precision Aggregated Local Models
Adam M. Edwards, Robert B. Gramacy
Large scale Gaussian process (GP) regression is infeasible for larger data sets due to cubic scaling of flops and quadratic storage involved in working with covariance matrices. Re…
Analyzing Stochastic Computer Models: A Review with Opportunities
Evan Baker, Pierre Barbillon, Arindam Fadikar +10
In modern science, computer models are often used to understand complex phenomena, and a thriving statistical community has grown around analyzing them. This review aims to bring a…
Distance-distributed design for Gaussian process surrogates
Boya Zhang, D. Austin Cole, Robert B. Gramacy
A common challenge in computer experiments and related fields is to efficiently explore the input space using a small number of samples, i.e., the experimental design problem. Much…
On-site surrogates for large-scale calibration
Jiangeng Huang, Robert B. Gramacy, Mickael Binois +1
Motivated by a computer model calibration problem from the oil and gas industry, involving the design of a honeycomb seal, we develop a new Bayesian methodology to cope with limita…
Assessing Bayes factor surfaces using interactive visualization and computer surrogate modeling
Christopher T. Franck, Robert B. Gramacy
Bayesian model selection provides a natural alternative to classical hypothesis testing based on p-values. While many papers mention that Bayesian model selection is frequently sen…
Parameter and Uncertainty Estimation for Dynamical Systems Using Surrogate Stochastic Processes
M. Chung, M. Binois, R. B. Gramacy +3
Inference on unknown quantities in dynamical systems via observational data is essential for providing meaningful insight, furnishing accurate predictions, enabling robust control,…
A Case Study Competition Among Methods for Analyzing Large Spatial Data
Matthew J. Heaton, Abhirup Datta, Andrew Finley +10
The Gaussian process is an indispensable tool for spatial data analysts. The onset of the "big data" era, however, has lead to the traditional Gaussian process being computationall…
Replication or exploration? Sequential design for stochastic simulation experiments
Mickael Binois, Jiangeng Huang, Robert B Gramacy +1
We investigate the merits of replication, and provide methods for optimal design (including replicates), with the goal of obtaining globally accurate emulation of noisy computer si…
Practical heteroskedastic Gaussian process modeling for large simulation experiments
Mickael Binois, Robert B. Gramacy, Michael Ludkovski
We present a unified view of likelihood based Gaussian progress regression for simulation experiments exhibiting input-dependent noise. Replication plays an important role in that…
Modeling an Augmented Lagrangian for Blackbox Constrained Optimization
Robert B. Gramacy, Genetha A. Gray, Sebastien Le Digabel +4
Constrained blackbox optimization is a difficult problem, with most approaches coming from the mathematical programming literature. The statistical literature is sparse, especially…
Efficient Bayesian inference for long memory processes
Timothy Graves, Robert B. Gramacy, Christian Franzke +1
In forecasting problems it is important to know whether or not recent events represent a regime change (low long-term predictive potential), or rather a local manifestation of long…
Local Gaussian process approximation for large computer experiments
Robert B. Gramacy, Daniel W. Apley
We provide a new approach to approximate emulation of large computer experiments. By focusing expressly on desirable properties of the predictive equations, we derive a family of l…
Dynamic trees for streaming and massive data contexts
Christoforos Anagnostopoulos, Robert B. Gramacy
Data collection at a massive scale is becoming ubiquitous in a wide variety of settings, from vast offline databases to streaming real-time information. Learning algorithms deploye…
The Importance of Prior Choice in Model Selection: a Density Dependence Example
James D. Lawrence, Dr. Robert B. Gramacy, Dr. Len Thomas +1
We perform a Bayesian analysis on abundance data for ten species of North American duck, using the results to investigate the evidence in favour of biologically motivated hypothese…