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.
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References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Guhaniyogi, Rajarshi; Dunson, David B.: Compressed Gaussian process for manifold regression (2016)
- Kang, Lulu; Joseph, V.Roshan: Kernel approximation: from regression to interpolation (2016)
- Blake MacDoanld, Hugh Chipman, Pritam Ranjan: GPfit: An R package for Gaussian Process Model Fitting using a New Optimization Algorithm (2013) arXiv
- Gramacy, Robert B.; Taddy, Matt; Wild, Stefan M.: Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning (2013)
- Gramacy, Robert B.; Lee, Herbert K.H.: Cases for the nugget in modeling computer experiments (2012)
- Broderick, Tamara; Gramacy, Robert B.: Classification and categorical inputs with treed Gaussian process models (2011)
- Lee, Herbert K.H.; Gramacy, Robert B.; Linkletter, Crystal; Gray, Genetha A.: Optimization subject to hidden constraints via statistical emulation (2011)
- Gramacy, Robert B.; Lee, Herbert K.H.: Gaussian processes and limiting linear models (2008)