Characterizing the principle of minimum cross-entropy within a conditional-logical framework

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• 作者：Kern-Isberner ; Gabriele
• 关键词：Probabilistic reasoning ; Minimum cross-entropy ; Conditionals ; Knowledge representation ; Nonmonotonic reasoning ; Expert systems
• 刊名：Artificial Intelligence
• 出版年：1998
• 出版时间：January, 1998
• 年：1998
• 卷：98
• 期：1-2
• 页码：169-208
• 全文大小：1.95 M

文摘

The principle of minimum cross-entropy (ME-principle) is often used as an elegant and powerful tool to build up complete probability distributions when only partial knowledge is available. The inputs it may be applied to are a prior distribution P and some new information R, and it yields as a result the one distribution P^* that satisfies R and is closest to P in an information-theoretic sense. More generally, it provides a “best” solution to the problem “How to adjust P to R?”