In this paper, we consider one aspect of the problem of applying
<I>decision theory</I> to the design of agents that learn how
to make decisions under uncertainty. This aspect concerns
how an agent can estimate probabilities for the possible states 
of the world, given that it only makes limited observations
before committing to a decision. 
 We show that the naive application of statistical tools can be improved upon
if the agent can determine which of his observations are truly
relevant to the estimation problem at hand. We give a framework in
which such determinations can be made, and define an estimation procedure
to use them. Our framework also suggests several extensions,
which show how additional knowledge can be used 
to improve the estimation procedure still further.
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