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Categorical Inputs, Sensitivity Analysis, Optimization and Importance Tempering with tgp Version 2, an R Package for Treed Gaussian Process Models

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  • Gramacy, Robert B.
  • Taddy, Matthew Alan

Abstract

This document describes the new features in version 2.x of the tgp package for R, implementing treed Gaussian process (GP) models. The topics covered include methods for dealing with categorical inputs and excluding inputs from the tree or GP part of the model; fully Bayesian sensitivity analysis for inputs/covariates; sequential optimization of black-box functions; and a new Monte Carlo method for inference in multi-modal posterior distributions that combines simulated tempering and importance sampling. These additions extend the functionality of tgp across all models in the hierarchy: from Bayesian linear models, to classification and regression trees (CART), to treed Gaussian processes with jumps to the limiting linear model. It is assumed that the reader is familiar with the baseline functionality of the package, outlined in the first vignette (Gramacy 2007).

Suggested Citation

  • Gramacy, Robert B. & Taddy, Matthew Alan, 2010. "Categorical Inputs, Sensitivity Analysis, Optimization and Importance Tempering with tgp Version 2, an R Package for Treed Gaussian Process Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i06).
    • Handle: RePEc:jss:jstsof:v:033:i06
    DOI: http://hdl.handle.net/10.18637/jss.v033.i06
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    References listed on IDEAS

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    1. Storlie, Curtis B. & Swiler, Laura P. & Helton, Jon C. & Sallaberry, Cedric J., 2009. "Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1735-1763.
    2. Storlie, Curtis B. & Helton, Jon C., 2008. "Multiple predictor smoothing methods for sensitivity analysis: Description of techniques," Reliability Engineering and System Safety, Elsevier, vol. 93(1), pages 28-54.
    3. Gramacy, Robert B., 2007. "tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 19(i09).
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    2. Savitsky, Terrance D., 2016. "Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i02).
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    5. Jager, Henriette I., 2014. "Thinking outside the channel: Timing pulse flows to benefit salmon via indirect pathways," Ecological Modelling, Elsevier, vol. 273(C), pages 117-127.
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    7. Roustant, Olivier & Ginsbourger, David & Deville, Yves, 2012. "DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i01).
    8. Krug, Rainer M. & Richardson, David M., 2014. "Modelling the effect of two biocontrol agents on the invasive alien tree Acacia cyclops—Flowering, seed production and agent survival," Ecological Modelling, Elsevier, vol. 278(C), pages 100-113.

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