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摘要翻译：
本文在Dezert-Smarandache理论(DSmT)框架下，提出了一种新的概率变换DSmP,以便从定义在任意识别框架模型上的任意基本信念分配建立主观概率测度。给出了几个例子来说明DSmP变换是如何工作的，并将其与文献中提出的主要变换进行了比较。我们从概率信息量(PIC)的角度证明了DSmP与经典变换相比的优势。本文还给出了该变换在定性信念分配中的直接推广。
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英文标题：
《A new probabilistic transformation of belief mass assignment》
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作者：
Jean Dezert (ONERA), Florentin Smarandache
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最新提交年份：
2008
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
In this paper, we propose in Dezert-Smarandache Theory (DSmT) framework, a new probabilistic transformation, called DSmP, in order to build a subjective probability measure from any basic belief assignment defined on any model of the frame of discernment. Several examples are given to show how the DSmP transformation works and we compare it to main existing transformations proposed in the literature so far. We show the advantages of DSmP over classical transformations in term of Probabilistic Information Content (PIC). The direct extension of this transformation for dealing with qualitative belief assignments is also presented.
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PDF链接：
https://arxiv.org/pdf/0807.3669
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