Not sure the priors are so wide.
from scipy import stats
smp = stats.norm.rvs(1.7, 1.7 * 0.01, 10000)
print(smp.min(), smp.max())
1.647 1.766
When you say too many parameters, what do you propose? I have a trimodal distribution that I’m modeling with 2 betas and one gamma. Each distr has 3-4 parameters. Open to ideas on how to simplify.
Point taken regarding normalizing the data. Regarding few datapoints in the observed data, I thought the implication of few points in the observed data is just that posterior distribution won’t be that different from the prior predictive.