The generalization ability of feature based prediction models will be badly hurt, when the features do not match between testing and training.
in the algebra view of likelihood, variance parameter is just the weight of training instance (i.e., the interaction between "entity pair and feature" or "entity pair and label"), measuring the importance for its contribution to the total likelihood.