From a response on the Stan help list:
Yes, indeed, I think it would be a good idea to reduce the scale on priors of the form U(0,100) or N(0,100^2). This won’t solve all problems but it can’t hurt.
If the issue is that the variance parameter can be very small in the estimation, yes, one approach would be to put in a prior that keeps the variance away from 0 (lognormal, gamma, whatever), another approach would be to use the Matt trick. Some mixture of these ideas might help.
And, by the way: when you do these things it might feel like an awkward bit of kluging to play around with the model to get it to convert properly. But the kluges of today are the textbook solutions of tomorrow. When it comes to statistical modeling, we’re living in beta-test world; we should appreciate the opportunities this gives us!