R package tgp: Bayesian treed Gaussian process models. Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes (GPs) with jumps to the limiting linear model (LLM). Special cases also implemented include Bayesian linear models, CART, treed linear models, stationary separable and isotropic GPs, and GP single-index models. Provides 1-d and 2-d plotting functions (with projection and slice capabilities) and tree drawing, designed for visualization of tgp-class output. Sensitivity analysis and multi-resolution models are supported. Sequential experimental design and adaptive sampling functions are also provided, including ALM, ALC, and expected improvement. The latter supports derivative-free optimization of noisy black-box functions.

References in zbMATH (referenced in 39 articles , 1 standard article )

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  1. Davis, Casey B.; Hans, Christopher M.; Santner, Thomas J.: Prediction of non-stationary response functions using a Bayesian composite Gaussian process (2021)
  2. Evandro Konzen, Yafeng Cheng, Jian Qing Shi: Gaussian Process for Functional Data Analysis: The GPFDA Package for R (2021) arXiv
  3. Luo, Zhao Tang; Sang, Huiyan; Mallick, Bani: A Bayesian contiguous partitioning method for learning clustered latent variables (2021)
  4. Xiao, Qian; Xu, Hongquan: A mapping-based universal kriging model for order-of-addition experiments in drug combination studies (2021)
  5. Hu, Ruimeng: Deep learning for ranking response surfaces with applications to optimal stopping problems (2020)
  6. Monterrubio-Gómez, Karla; Roininen, Lassi; Wade, Sara; Damoulas, Theodoros; Girolami, Mark: Posterior inference for sparse hierarchical non-stationary models (2020)
  7. Volodina, Victoria; Williamson, Daniel: Diagnostics-driven nonstationary emulators using kernel mixtures (2020)
  8. Yang, F.; Lin, C. Devon; Ranjan, P.: Global fitting of the response surface via estimating multiple contours of a simulator (2020)
  9. Daniel Turek, Mark Risser: Bayesian nonstationary Gaussian process modeling: the BayesNSGP package for R (2019) arXiv
  10. Letham, Benjamin; Karrer, Brian; Ottoni, Guilherme; Bakshy, Eytan: Constrained Bayesian optimization with noisy experiments (2019)
  11. Liang, Waley W. J.; Lee, Herbert K. H.: Bayesian nonstationary Gaussian process models via treed process convolutions (2019)
  12. McClarren, Ryan G.: Uncertainty quantification and predictive computational science. A foundation for physical scientists and engineers (2019)
  13. Mickaël Binois and Victor Picheny: GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis (2019) not zbMATH
  14. Seongil Jo; Taeryon Choi; Beomjo Park; Peter Lenk: bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors (2019) not zbMATH
  15. Yu, Yang; Zou, Zhihong; Wang, Shanshan; Meyer, Renate: Bayesian nonparametric modelling of the link function in the single-index model using a Bernstein-Dirichlet process prior (2019)
  16. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  17. Gladish, Daniel W.; Pagendam, Daniel E.; Peeters, Luk J. M.; Kuhnert, Petra M.; Vaze, Jai: Emulation engines: choice and quantification of uncertainty for complex hydrological models (2018)
  18. Ludkovski, Mike; Risk, Jimmy; Zail, Howard: Gaussian process models for mortality rates and improvement factors (2018)
  19. Marmin, Sébastien; Ginsbourger, David; Baccou, Jean; Liandrat, Jacques: Warped Gaussian processes and derivative-based sequential designs for functions with heterogeneous variations (2018)
  20. Schulz, Eric; Speekenbrink, Maarten; Krause, Andreas: A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions (2018)

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