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From a Bayesian decision theoretic framework, we show that the reason why the usual statistical approaches do not take context into account is because of the assumptions made on the joint prior probability function and because of the simplistic loss function chosen. We illustrate how the constraints sometimes employed by artificial intelligence researchers constitute a different kind of assumption on the joint prior probability function. We discuss a couple of loss functions which do take context into account and when combined with the joint prior probability constraint create a decision problem requiring a combinatorial state space search. We also give a theory for how probabilistic relaxation works from a Bayesian point of view.