tgp (version 1.0-1)

tgp.default.params: Default Treed Gaussian Process Model Parameters

Description

Construct a default list of parameters to the tgp function-- the generic interface to treed Gaussian process modeling

Usage

tgp.default.params(d)

Arguments

d

number of input dimensions dim(X)[2]

Value

• The output is the following list of params...
• corr
  "expsep" separable power exponential family correlation model; alternate is "exp" isotropic power family
• bprior
  Linear (beta) prior, default is "bflat"; alternates include "b0" hierarchical Normal prior, "bmle" empirical Bayes Normal prior, "bcart" Bayesian linear CART style prior from Chipman et al, "b0tau" a independent Normal prior with inverse-gamma variance.
• start
  c(0.5,0.1,1.0,1.0) starting values for range, nugget, s2, and tau2
• beta
  rep(0,d) starting values for beta linear parameters
• tree
  c(0.25,2,10) tree prior process parameterization c(alpha, beta, minpart) specifying p(split leaf of depth eta) = alpha*(1+eta)^beta with zero probability to trees with partitions containing less than minpart data points
• s2.p
  c(5,10) s2 inverse-gamma prior parameterization c(a0, g0) where g0 is scale (1/rate) parameter
• tau2.p
  c(5,10) tau2 inverse-gamma prior parameterization c(a0, g0) where g0 is scale (1/rate) parameter
• d.p
  c(1.0,20.0,10.0,10.0) Mixture of gamma prior parameter (initial values) for for the range parameter c(a1,g1,a2,g2) where g1 and g2 are scale (1/rate) parameters
• d.p
  code{c(1,1,1,1)} Mixture of gamma prior parameter (initial values) for for the range parameter c(a1,g1,a2,g2) where g1 and g2 are scale (1/rate) parameters; default reduces to simple exponential prior
• gamma
  c(10,0.2,10) Limiting Linear model parameters c(g, t1, t2), with growth parameter g > 0 minimum parameter t1 >= 0 and maximum parameter t1 >= 0, where t1 + t2 <= 1<="" code=""> specifies p(b|d) = t1 + exp[-g*(t2-t1)/(d-0.5)]
• d.lam
  "fixed" Hierarchical exponential distribution parameters to a1, g1, a2, and g2 of the prior distribution for the range parameter d.p; fixed indicates that the hierarchical prior is "turned off"
• nug.lam
  "fixed" Hierarchical exponential distribution parameters to a1, g1, a2, and g2 of the prior distribution for the nug parameter nug.p; "fixed" indicates that the hierarchical prior is "turned off"
• s2.lam
  c(0.2,10) Hierarchical exponential distribution prior for a0 and g0 of the prior distribution for the s2 parameter s2.p; "fixed" indicates that the hierarchical prior is "turned off"
• tau2.lam
  c(0.2,10) Hierarchical exponential distribution prior for a0 and g0 of the prior distribution for the s2 parameter tau2.p; "fixed" indicates that the hierarchical prior is "turned off"

References

Gramacy, R. B., Lee, H. K. H., & Macready, W. (2004). Parameter space exploration with Gaussian process trees. ICML (pp. 353--360). Omnipress & ACM Digital Library.

Gramacy, R. B., Lee, H. K. H., & Macready, W. (2005). Adaptive Exploration of Computer Experiment Parameter Spaces. submitted to JCGS, available as UCSC Technical Report ams2005-16 http://www.ams.ucsc.edu/reports/trview.php?content=view&name=ams2005-16

Gramacy, R. B. & Lee, H. K. H. (2005). Gaussian Processes and Limiting Linear Models. available as UCSC Technical Report ams2005-17 http://www.ams.ucsc.edu/reports/trview.php?content=view&name=ams2005-17

http://people.ucsc.edu/~boobles/tgp.php

See Also

tgp