Is there a way to not use an explicit loop?
Both shape parameters of rbeta can be vectors; for
x <- rbeta(n, shape1, shape2)
x[i] ~ Beta(shape1[i], shape2[i])
so
bbsim <- function(m=1000, num.post.draws=1e4, size.a=100, prob.a=.27, prior.count=1) {
data.count <- rbinom(m, size.a, prob.a)
shape1 <- rep(prior.count + data.count, each=num.post.draws)
shape2 <- rep(prior.count + size.a - data.count, each=num.post.draws)
matrix(rbeta(m * num.post.draws, shape1, shape2), num.post.draws, m)
}
Then you can do
beta.draws <- bbsim()
means <- apply(beta.draws, 2, mean)
medians <- apply(beta.draws, 2, median)
etc
Dan
On Wed, Sep 17, 2008 at 11:56:36AM -0700, Juancarlos Laguardia wrote:
I have a problem in where i generate m independent draws from a binomial
distribution,
say
draw1 = rbinom( m , size.a, prob.a )
then I need to use each draw to generate a beta distribution. So, like
using a beta prior, binomial likelihood, and obtain beta posterior, m
many times. I have not found out a way to vectorize draws from a beta
distribution, so I have an explicit for loop within my code
for( i in 1: m ) {
beta.post = rbeta( 10000, draw1[i] + prior.constant , prior.constant +
size.a - draw1[i] )
beta.post.mean[i] = mean(beta.post)
beta.post.median[i] = median(beta.post)
etc.. for other info
}
Is there a way to vectorize draws from an beta distribution?
UC Slug
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