Nothing
library(dream)
## n-dimensional banana shaped gaussian distribution
## Hyperbolic shaped posterior probability distribution
## @param x vector of length ndim
## @param bpar banana-ness. scalar.
## @param imat matrix ndim x ndim
## @return SSR. scalar
Banshp <- function(x,bpar,imat){
x[2] <- x[2]+ bpar * x[1]^2 - 100*bpar
S <- -0.5 * (x %*% imat) %*% as.matrix(x)
return(S)
}
control <- list(
ndim=10,
DEpairs=3,
gamma=0,
nCR=3,
ndraw=1e5,
steps=10,
eps=5e-2,
outlierTest='IQR_test',
pCR.Update=TRUE,
thin.t=10,
boundHandling='none',
burnin.length=Inf, ##for compatibility with matlab code
REPORT=1e4 ##reduce frequency of progress reports
)
pars <- replicate(control$ndim,c(-Inf,Inf),simplify=F)
cmat <- diag(control$ndim)
cmat[1,1] <- 100
bpar <- 0.1
imat <- solve(cmat)
muX=rep(0,control$ndim)
qcov=diag(control$ndim)*5
set.seed(11)
dd <- dream(
FUN=Banshp, func.type="logposterior.density",
pars = pars,
FUN.pars=list(
imat=imat,
bpar=bpar),
INIT = CovInit,
INIT.pars=list(
muX=muX,
qcov=qcov,
bound.handling=control$boundHandling
),
control = control
)
summary(dd)
## Show banana
library(lattice)
ddm <- as.matrix(window(dd))
plot(ddm[,1],ddm[,2])
splom(ddm)
## Results were compared to two matlab runs
## While banana doesn't appear to match, it appears this is because it is a difficult problem
## Separate matlab results do not match either
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