Bayesian treed Gaussian process models (tgp R package)
- Bayesian linear models, CART, treed linear models, stationary separable and isotropic Gaussian processes, and Gaussian process single-index models are also implemented
- Categorical inputs, sensitivity analysis, multi-resolution models and importance tempering are supported
- Includes methods for the (sequential) design of exeperiments under these models
- via treed sequential maximum entropy design, combined with active learning (ALM and ALC)
- via the expected improvement statistic for the derivative-free optimization of noisy black-box functions
- 1-d and 2-d plotting, with higher dimension projection and slice capabilities, and tree drawing functions are also provided for visualization of tgp-class output.
- Obtain R from cran.r-project.org by selecting the version for your operating system.
- Install the tgp package, from within R. This
will download, install, and configure the tgp package for
- Optionally, install the akima and maptree packages.
> install.packages(c("akima", "maptree"))
- Load the library as you would for any R library.
- The tgp
as a package vignette, authored in Sweave. The pdf can be
obtained from within R with the following code.
To obtain the source code contained in the vignette, use the Stangle command.
- See the package documentation. A pdf version of the
reference manual, or help pages, is also available.
The help pages can be accessed from within
R. Try starting with...
> ? btgp # follow the examples
- I gave a poster at the Valencia 8 meeting (June 2006) which is a (very) condensed version of the tutorial, above.
> Stangle(vignette("tgp")$file)Each of the examples in the vignette are also available as a demo. For example, to get the demo corresponding the example for the exponential data, do:
> demo("exp", package="tgp")The demos were actually created using the Stangle command on the vignette sources. To see all available demos, type:
> demo(package="tgp")Version 2.x is accompanied by a new tutorial outlining the extentions of the methods to categorical inputs, sensitivity analysis, optimization, and importance tempering.
- Gaussian process single-index models as emulators for computer experiments (2012) with Heng Lian; Technometrics, 54(1), pp. 30-41; preprint on arXiv:1009.4241
- Categorical inputs, sensitivity analysis, optimization and importance tempering with tgp version 2, an R package for treed Gaussian process models (2010) with Matt Taddy. Journal of Statistical Software, 33(6); snapshot of one of two R vignettes in the tgp package as of January 2010
- tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models. (2007) Journal of Statistical Software, volume 19(9). Snapshot of the R vignette for the tgp package as of June 2007.
- Bayesian treed Gaussian process models with an application to computer modeling (2008) with Herbert K.H. Lee. Journal of the American Statistical Association, 103(483), pp. 1119-1130; preprint on arXiv:0710.4536
- Gaussian Processes and Limiting Linear Models. (2008) with Herbert K.H. Lee. Computational Statistics and Data Analysis, 53, pp. 123-136; preprint on arXiv:0804.4685
- Adaptive Design and Analysis of Supercomputer Experiments (2009) with Herbert K.H. Lee. Technometrics, 51(2), pp. 130-145; preprint on arXiv:0805.4359
- My Ph.D. thesis. More details than you need, all in one place.
- Parameter space exploration with Gaussian process trees with Herbert K. H. Lee and William G. Macready; International Conference on Machine Learning (ICML 2004) Banff, Canada.
Please send questions and comments to rbgramacy_AT (_ams_DOT_ucsc_DOT_edu). Enjoy!
Robert B. Gramacy -- 2006