Gramacy, Robert B.

(as bibtex)

Elsewhere: [math people] [google] [google scholar]

Articles (22)

• Weikel, Daniel, Stewart-Ibarra, Anna M., Ryan, Sadie J., Rohr, Jason, Murdock, Courtney, Mordecai, Erin, Cohen, Jeremy, Gramacy, Robert B., Johnson, Leah R.. Phenomenological forecasting of disease incidence using heteroskedastic Gaussian processes: A dengue case study. The Annals of Applied Statistics 2018. 12:27-66
• Gramacy, Robert B.. Comment on Article by Pratola. Bayesian Analysis 2016. 11:913-919
• Gramacy, Robert B.. laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R. Journal of Statistical Software 2016. 72:1-46
• Drake, R. Paul, Trantham, Matt, Rutter, Erica, Kuranz, Carolyn C., Grosskopf, Michael J., Holloway, James Paul, Bingham, Derek, Gramacy, Robert B.. Calibrating a large computer experiment simulating radiative shock hydrodynamics. The Annals of Applied Statistics 2015. 9:1141-1168
• Gramacy, Robert B., Niemi, Jarad, Weiss, Robin M.. Massively Parallel Approximate Gaussian Process Regression. SIAM/ASA Journal on Uncertainty Quantification 2014. 2:564-584
• Wild, Stefan M., Taddy, Matt, Gramacy, Robert B.. Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning. The Annals of Applied Statistics 2013. 7:51-80
• McCulloch, Robert, Gramacy, Robert B., George, Edward I., Chipman, Hugh. Bayesian treed response surface models. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2013. 3:298-305
• Gramacy, Robert B., Jensen, Shane T., Taddy, Matt. Estimating player contribution in hockey with regularized logistic regression. Journal of Quantitative Analysis in Sports 2013. 9:97-111
• Gramacy, Robert B., Polson, Nicholas G.. Simulation-based regularized logistic regression. Bayesian Analysis 2012. 7:567-590
• Broderick, Tamara, Gramacy, Robert B.. Classification and categorical inputs with Treed Gaussian process models. Journal of Classification 2011. 28:244-270
• Taddy, Matthew A., Gramacy, Robert B., Polson, Nicholas G.. Dynamic trees for learning and design. Journal of the American Statistical Association 2011. 106:109-123
• Gramacy, Robert B., Polson, Nicholas G.. Particle learning of Gaussian process models for sequential design and optimization. Journal of Computational and Graphical Statistics 2011. 20:102-118
• Gramacy, Robert B., Pantaleo, Ester. Shrinkage regression for multivariate inference with missing data, and an application to portfolio balancing. Bayesian Analysis 2010. 5:237-262
• Merl, Daniel, Johnson, Leah R., Gramacy, Robert B., Mangel, Marc. Amei: An R package for the adaptive management of epidemiological interventions. Journal of Statistical Software 2010. 36:NA-NA
• Lee, Herbert K. H., Taddy, Matthew, Gramacy, Robert B., Gray, Genetha A.. Designing and analysing a circuit device experiment using treed Gaussian processes.
• Gramacy, Robert B., Taddy, Matthew Alan. Categorical inputs, sensitivity analysis, optimization and importance tempering with tgp version 2, an R package for treed Gaussian process models. Journal of Statistical Software 2010. 33:NA-NA
• Pantaleo, Ester, Gramacy, Robert B.. Shrinkage regression for multivariate inference with missing data, and an application to portfolio balancing. Bayesian Analysis 2010. 5:237-262
• Gramacy, Robert B., Lee, Herbert K. H.. Adaptive Design and Analysis of Supercomputer Experiments. Technometrics 2009. 51:130-145
• Cule, Madeleine, Gramacy, Robert B., Samworth, Richard. LogConcDEAD: An R Package for Maximum Likelihood Estimation of a Multivariate Log-Concave Density. Journal of Statistical Software 2009. 29:1-20
• Gramacy, Robert B., Lee, Herbert K. H.. Bayesian Treed Gaussian Process Models With an Application to Computer Modeling. Journal of the American Statistical Association 2008. 103:1119-1130
• Gramacy, Robert B.. tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models. Journal of Statistical Software 2007. 19:1-46
• Gramacy, Robert B., Lee, Herbert K.H.. Gaussian processes and limiting linear models.