# tgp v2.4-14

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## 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.

## Functions in tgp

 Name Description lhs Latin Hypercube sampling default.itemps Default Sigmoidal, Harmonic and Geometric Temperature Ladders tgp.design Sequential Treed D-Optimal Design for Treed Gaussian Process Models exp2d.Z Random Z-values for 2-d Exponential Data tgp.default.params Default Treed Gaussian Process Model Parameters interp.loess Lowess 2-d interpolation onto a uniform grid tgp-package The Treed Gaussian Process Model Package optim.tgp Surrogate-based optimization of noisy black-box function friedman.1.data First Friedman Dataset and a variation plot.tgp Plotting for Treed Gaussian Process Models mapT Plot the MAP partition, or add one to an existing plot tgp-internal Internal Treed Gaussian Process Model Functions predict.tgp Predict method for Treed Gaussian process models btgp Bayesian Nonparametric \& Nonstationary Regression Models exp2d 2-d Exponential Data sens Monte Carlo Bayesian Sensitivity Analysis exp2d.rand Random 2-d Exponential Data itemps Functions to plot summary information about the sampled inverse temperatures, tree heights, etc., stored in the traces of a "tgp"-class object tgp.trees Plot the MAP Tree for each height encountered by the Markov Chain dopt.gp Sequential D-Optimal Design for a Stationary Gaussian Process partition Partition data according to the MAP tree No Results!