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Thank you Pablo for this solution, but how would I generate "replicates" of
the first element in your data-vector (i.e. 2) coming from the posterior
predictive distribution?

Dipankar




On Thu, Feb 2, 2012 at 10:16 AM, Dr. Pablo E. Verde <
[log in to unmask]> wrote:

> **
>
>  Hi Dipankar,
>
> The answer is not, you cannot sample from the predictive posterior in your
> way.
>
> The easy way to sample from the predictive posterior while you using the
> zeros trick is
> by adding artificial missing data to your data vector.
>
> So if your data set is in a list:
>
> list(x =c(2,5,6,2,4,7,8))
>
> Then do
>
> list(x =c(2,5,6,2,4,7,8, NA, , NA, NA, NA, NA))
>
> the values of x[8:12] are sampled automatically from the predictive
> posterior.
>
> Hope it helps,
>
> Pablo
>
>
>
> ----- Original Message -----
> *From:* Dipankar Bandyopadhyay <[log in to unmask]>
> *To:* [log in to unmask]
> *Sent:* Tuesday, January 31, 2012 8:35 PM
> *Subject:* [BUGS] generating replicates from zeros trick
>
>  Hi,
>
> I am interested in generating replicates from the posterior predictive
> distribution of a r.v. X, which has been programmed in WinBUGS using the
> zeros trick. Can someone suggest whether I am doing it correctly? So,
> zeros.rep[] in the code below are my replicates.
>
>
> K <- 1000
>  ll[i] <- ......   ##  Log-likelihood of X
> zeros[i] < - 0
> zeros[i] ~   dpois(phi[i])  ##  Zeros trick
> phi[i]  <- - ll[i] + K
> zeros.rep[i] ~ dpois(phi[i])   ##  Replicates of y[i] ?
>
>
> Thanks a bunch,
> Dipankar
>
>
>  *______________________________
> *
> *
> Dipankar Bandyopadhyay, PhD
> *
> *Associate Professor,*
> *Division of Biostatistics, School of Public Health,**
> **University of Minnesota *
> *Minneapolis, MN 55455 USA*
>
>  *http://www.biostat.umn.edu/~dipankar*<http://www.biostat.umn.edu/~dipankar>
>
>
>
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