Uncertain Reasoning Using Maximum Entropy Inference
This work provides a theoretical foundation for uncertain reasoning, potentially benefiting researchers in AI and statistics, though it appears incremental in refining existing concepts.
The paper addresses an objection to the information-theoretic justification for maximum entropy inference in uncertain reasoning and argues that combining it with probability theory forms a complete, well-grounded theory, outperforming other methods.
The use of maximum entropy inference in reasoning with uncertain information 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 maximum entropy inference with certain other currently popular methods for uncertain reasoning. 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. It is argued that maximum entropy is a dynamic theory and that a complete 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 better grounded than are other theories of uncertain reasoning.