#*******************************************************************************
#
# Bayesian Regression and Adaptive Sampling with Gaussian Process Trees
# Copyright (C) 2005, University of California
#
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation; either
# version 2.1 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with this library; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
#
# Questions? Contact Robert B. Gramacy (rbgramacy@ams.ucsc.edu)
#
#*******************************************************************************
"tgp.default.params" <-
function(col, base="gp")
{
if(base == "mrgp") col=2*(col-1)
params <-
list(
base=base,
tree=c(0.25,2,10), # tree prior params <alpha> and <beta>
bprior="bflat", # linear prior (b0, bmle, bflat, bcart or b0tau)
beta=rep(0,col), # start vals beta (length = col = dim + 1)
start=c(1.0,1.0), # start vals for s2, and tau2
s2.p=c(5,10), # s2 prior params (initial values) <a0> and <g0>
s2.lam=c(0.2,10), # s2 hierarc inv-gamma prior params (or "fixed")
tau2.p=c(5,10), # tau2 prior params (initial values) <a0> and <g0>
tau2.lam=c(0.2,0.1), # tau2 hierarch inv-gamma prior params (or "fixed")
corr="expsep", # correllation model (exp, or expsep)
cstart=c(0.1, 0.5), # start vals for nug and d
nug.p=c(1,1,1,1), # nug gamma-mix prior params (initial values)
nug.lam="fixed", # nug hierarch gamma-mix prior params (or "fixed")
gamma=c(10,0.2,0.7), # gamma linear pdf parameter
d.p=c(1.0,20.0,10.0,10.0), # d gamma-mix prior params (initial values)
delta.p=c(), # delta parameter for high fidelity variance
nugf.p=c(), # residual process nugget gamma-mix prior params
d.lam="fixed", # d lambda hierarch gamma-mix prior params (or "fixed")
nu=c() # matern correlation smoothing parameter
)
if(base == "mrgp"){
mrd.p <- c(1,100,1,20) # add in the gamma-mix params for the residual process
params$d.p =c(params$d.p, mrd.p)
params$delta.p=c(2,2,2,2)
params$nugf.p=c(1,20,1,1)
}
return(params)
}