hmmeantemp | R Documentation |
This function provides another toy illustration of the capabilities of a tempered random walk Metropolis-Hastings algorithm applied to the posterior distribution associated with a two-component normal mixture with only its means unknown (Chapter 7). It shows how a decrease in the temperature leads to a proper exploration of the target density surface, despite the existence of two well-separated modes.
hmmeantemp(dat, niter, var = 1, alpha = 1)
dat |
set to be modelled as a mixture |
niter |
number of iterations |
var |
variance of the random walk |
alpha |
temperature, expressed as power of the likelihood |
When \alpha=1
the function operates (and can be used) as a regular Metropolis-Hastings algorithm.
sample of \mu_i
's as a matrix of size niter
x 2
dat=plotmix(plot=FALSE)$sample
simu=hmmeantemp(dat,1000)
plot(simu,pch=19,cex=.5,col="sienna",xlab=expression(mu[1]),ylab=expression(mu[2]))
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