Bayesian Treed Gaussian Process Models
Bayesian Nonparametric & Nonstationary Regression Models
Default Sigmoidal, Harmonic and Geometric Temperature Ladders
Sequential D-Optimal Design for a Stationary Gaussian Process
Random 2-d Exponential Data
Random Z-values for 2-d Exponential Data
First Friedman Dataset and a variation
Lowess 2-d interpolation onto a uniform grid
Functions to plot summary information about the sampled inverse temper...
Latin Hypercube sampling
Plot the MAP partition, or add one to an existing plot
Surrogate-based optimization of noisy black-box function
Partition data according to the MAP tree
Plotting for Treed Gaussian Process Models
Predict method for Treed Gaussian process models
Monte Carlo Bayesian Sensitivity Analysis
Internal Treed Gaussian Process Model Functions
The Treed Gaussian Process Model Package
Default Treed Gaussian Process Model Parameters
Sequential Treed D-Optimal Design for Treed Gaussian Process Models
Plot the MAP Tree for each height encountered by the Markov Chain
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. For details and tutorials, see Gramacy (2007) <doi:10.18637/jss.v019.i09> and Gramacy & Taddy (2010) <doi:10.18637/jss.v033.i06>.