Flat and weak prior distributions are two possible types of prior in Bayesian statistics. In layman's terms, what are the key differences between the two, and why do we use one or the other?
Specifically, I use Bayesian methods to estimate variance and covariance components and I am looking for a clear simple way to explain the differences to non-statisticians/Bayesians.
Laymans description of Inverse-Wishart and Parameter-Expanded priors would also be of use! :)