Hello,
I am doing mcmc=10000 simulations from a posterior distribution of the parameters
of a mixture of K=6 normal densities.
I have mcmc by K matrices simMeans, simVars and simWeights containing
the simulation output: one row for each simulation, one column for
each normal component of the mixture.
One thing I would like to do is a plot of the posterior predictive
density. In order to do that I define a vector x of points at which I
want to evaluate this density. And then I use the following commands:
pred <- colMeans(apply(structure(dnorm(rep(x,each=mcmc*K), mean=simMeans,
sd=sqrt(simVars))*
rep(simWeights,length(x)),dim=c(mcmc,K,length(x))),c(1,3),sum))
lines(x,pred)
Everything works, but it is very slow. Does anybody have suggestions
to do the same thing in a more efficient way?
Thanks in advance,
Giovanni Petris
--
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[ Giovanni Petris [EMAIL PROTECTED] ]
[ Department of Mathematical Sciences ]
[ University of Arkansas - Fayetteville, AR 72701 ]
[ Ph: (479) 575-6324, 575-8630 (fax) ]
[ http://definetti.uark.edu/~gpetris/ ]
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