Uncertain Reasoning Using Maximum Entropy Inference

The use of maximum entropy infer­ ence in reasoning with uncertain informa­ tion is commonly justified by an information-theoretic argument. This paper discusses a possible objection to this information-theoretic justification and shows how it can be met. I then compare max­ imum entropy inference with certain other currently popular methods for uncertain rea­ soning. In making such a comparison, one must distinguish between static and dynamic theories of degrees of belief: a static theory concerns the consistency conditions for degrees of belief at a given time; whereas a dynamic theory concerns how one's degrees of belief should change in. the light of new information. lt is argued that maximum entropy is a dynamic theory and that a com­ plete theory of uncertain reasoning can be gotten by combining maximum entropy inference with probability theory, which is a static theory. This total theory, I argue, is much bet�r grounded than are other theories of uncertain reasoning.