# Uninformative priors for variance distribution in hierarchical bayesian models

I read that uninformative priors for population variances are often represented by invgamma(eps,eps) where eps could be 1, 0.1 or 0.001.

In my model I used these but variance sometimes goes upto infinity. Due to this, the prior of mean which is a truncated-normal distribution is disturbed as sample values from this prior variance distribution exceeds the expected variance. Thus, my MCMC-metropolis hastings-gibbs sampler failed to converge or there were some errors.

So, now I have used invgamma(2,2) as uninformative prior for variance because the sample values are not too high. But, is this distribution allowed for describing "uninformativeness"?

Please help, I am having a tough time making my bayesian inference - MCMC model converge to correct posterior distribution.