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原文連結
論文資訊
- 類型：已發表論文
- 日期：2022
摘要
The principle of maximum 熵, developed more than six decades ago, provides a systematic approach to modeling inference, and data analysis grounded in the principles of 資訊 theory, 貝氏 probability and constrained optimization. Since its formulation, criticisms about the consistency of that method and the role of constraints have been raised. Among these, the chief criticism is that maximum 熵 does not satisfy the principle of causation, or similarly, that maximum 熵 updating is inconsistent due to an inadequate representation of causal 資訊. We show that these criticisms rest on misunderstanding and misapplication of the way constraints have to be specified within the maximum 熵 method. Correction of these problems eliminates the seeming paradoxes and inconsistencies critics claim to have detected.
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