On 7/18/13 5:46 PM, Tom Wallis wrote:
> Hi Andrew,
>
> Thanks for the suggestion; I had tried that and it still causes "no acceptably small step size" errors. Fixing the
> parameters completely seems to allow the model to converge just fine, but makes the post-processing more difficult.
Do your parameters constraints match their effective ranges?
If you define something like theta ~ normal(a,0.001),
then you might want to define theta with a constraint such as:
real<lower=a-0.01, upper=a+0.01> theta;
which should give you +/- 10 sd and make things a bit more
stable.
> On Thu, Jul 18, 2013 at 11:30 PM, Andrew Gelman <
gel...@stat.columbia.edu <mailto:
gel...@stat.columbia.edu>> wrote:
>
> If you want to set a parameter theta to the fixed value A, you could always kluge it via: theta ~ normal(A,.001);
> As you might be aware, you can do this even if your parameter theta already has a prior. Stan doesn't care, it just
> adds all the components together to compute the log posterior.
Hopefully, it won't come to this.
...
>> e.g.:
>> log_c ~ normal(log(10), 1);
>> c <- exp(log_c);
You have to make sure c doesn't overflow, but that shouldn't be
a problem. But why not just use lognormal on c directly?
- Bob