Please is it ever possible for the prior distribution to contain more information about parameter(s) than the posterior distribution? If yes, when can that occur? Is it the same concept as the posterior being "diffuse with respect to the prior"? I am reading the paper on A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation by Blum et al. (2013) and I came across the concept of a diffuse posterior on page 6. I would appreciate an explanation.