低概率和高概率事件混合的Pignistic概率变换
Pignistic Probability Transforms for Mixes of Low- and High-Probability Events
摘要：
在一些现实世界的信息融合的情况下，时间关键的决策必须与不完整的信息集。置信函数理论（例如，证据理论（Dempster-Shafer theory of evidence，Transferable Belief Model）已经被证明为处理或融合形成不完全信息集的定量线索或信息度量提供了一种合理的方法。对于决策制定，pignistic（来自拉丁语pignus，赌注）概率变换已被证明是使用信念或基本信念分配（BBA）进行决策的好方法。对于许多系统，只需要处理集合中最可能的元素。对于某些关键系统，必须评估错误决策的风险，并为决策制定建立安全概率阈值。这增加了决策的复杂性，因为必须处理集合中高于风险决策阈值的所有元素。如果大多数概率都低于这个阈值，问题就大大简化了。找到一个适当地表示低概率和高概率事件混合的概率变换是至关重要的。本文介绍了四种新的pignistic概率变换，并使用最新的Beliefs、Plaufet或BBA值来改进pignistic概率估计。它们中的一些比Smets pignistic变换为更小的信念或BBA值分配更小的概率值。与Smets pignistic变换相比，它们还为较大的信念或BBA值分配更高的概率值。这些概率变换将分配一个概率值，该概率值更快地收敛到低于风险阈值的值。还引入了概率信息内容（PIC）变量，其将信息内容值分配给任何概率集合。定义了四个运算符以简化推导过程。
英文摘要：
In some real world information fusion situations, time critical decisions must be made with an incomplete information set. Belief function theories (e.g., Dempster-Shafer theory of evidence, Transferable Belief Model) have been shown to provide a reasonable methodology for processing or fusing the quantitative clues or information measurements that form the incomplete information set. For decision making, the pignistic (from the Latin pignus, a bet) probability transform has been shown to be a good method of using Beliefs or basic belief assignments (BBAs) to make decisions. For many systems, one need only address the most-probable elements in the set. For some critical systems, one must evaluate the risk of wrong decisions and establish safe probability thresholds for decision making. This adds a greater complexity to decision making, since one must address all elements in the set that are above the risk decision threshold. The problem is greatly simplified if most of the probabilities fall below this threshold. Finding a probability transform that properly represents mixes of low- and high-probability events is essential. This article introduces four new pignistic probability transforms with an implementation that uses the latest values of Beliefs, Plausibilities, or BBAs to improve the pignistic probability estimates. Some of them assign smaller values of probabilities for smaller values of Beliefs or BBAs than the Smets pignistic transform. They also assign higher probability values for larger values of Beliefs or BBAs than the Smets pignistic transform. These probability transforms will assign a value of probability that converges faster to the values below the risk threshold. A probability information content (PIC) variable is also introduced that assigns an information content value to any set of probability. Four operators are defined to help simplify the derivations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
