  In this paper, I proof that Importance Sampling estimates based on dependent
sample sets are consistent under certain conditions. This can be used to reduce
variance in Bayesian Models with factorizing likelihoods, using sample sets
that are much larger than the number of likelihood evaluations, a technique
dubbed Sample Inflation. I evaluate Sample Inflation on a toy Gaussian problem
and two Mixture Models.
