How to reduce bias between posterior and true values?

Questions v5
3

I’m defining a model for the “activity” parameter using a Gamma distribution like so

...
alpha_prior = pm.HalfNormal("alpha_prior", sigma=1)
beta_prior = pm.HalfNormal("beta_prior", sigma=1)
act = pm.Gamma("act", alpha=alpha_prior, beta=beta_prior)
...

I’ve also observed that when alpha and beta are defined manually with very small deviations, the results are generally more accurate. As an example, when alpha=10000 and beta=100, the posterior mean of the activity parameter is 100.43, which is very close to the true value of 100. This suggests that the underlying physical model I’m working with is correctly specified.