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I am trying to model a function of observed data in JAGS. For example

   x[i,t]/phi ~ dpois(mu)

where x is observed and phi is a parameter in the model. I can't seem to get this working and am wondering if I may be missing some larger issue in JAGS. I am pretty sure you can't have any transformation of the data in the model statement and need to use a data block but that doesn't seem to be dynamic, thus not allowing for me to fit phi.

Any ideas?

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  • $\begingroup$ Can you please add the formulas for your model? $\endgroup$ – teucer Nov 7 '11 at 10:23
  • $\begingroup$ it is a pretty convoluted model and I think it will complicate the question. Is my question unclear? $\endgroup$ – scottyaz Nov 7 '11 at 11:24
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    $\begingroup$ I believe you cannot fit the model that you have described: to my knowledge in JAGS you are not able to scale a distribution. What distribution $\phi$ is supposed to follow? If it is simple enough, you can derive the distribution of $x$ directly and sample from it. $\endgroup$ – teucer Nov 7 '11 at 11:48
  • $\begingroup$ @teucer Feel free to convert your comment to a response, so that it can be voted up and perhaps get the green mark. $\endgroup$ – chl Nov 7 '11 at 17:53
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So here the answer from the comment:

I believe there is no way to directly sample from a scaled distribution in JAGS, except the cases where the scaled distribution can be expressed as a standard distribution.

This being said there is trick, the so called "zero trick", but I have not used it so far. It might be useful in your case...

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