Is there any difference between specifying priors manually in code (model 1) or as input in the data section (model 2)? I suspect there isn’t because this is not the part being sampled but just want to make sure that this is correct.
I am asking because model 1 allows me to change the prior when running the stan code while model 2 means I have to change the priors in the stan code and save them manually. Any idea if there is a difference? Thanks.
Model 1:
data {
int <lower=0> rep_mean;
}
parameters {
real reporting_rate0;
}
model {
reporting_rate0 ~ normal(rep_mean, 1);
}
Model 2:
parameters {
real reporting_rate0;
}
model {
reporting_rate0 ~ normal(-2, 1);
}