ABSTRACT Recently, a new way of computing an expected value in the Dempster–Shafer theory of evidence was introduced by Prakash P. Shenoy. Up to now, when they needed the expected value of a utility function in D-S theory, the authors usually did it indirectly: first, they found a probability measure corresponding to the considered belief function, and then computed the classical probabilistic expectation using this probability measure. To the best of our knowledge, Shenoy's operator of expectation is the first approach that takes into account all the information included in the respective belief function. Its only drawback is its exponential computational complexity. This is why, in this paper, we compare five different approaches defining probabilistic representatives of belief function from the point of view, which of them yields the best approximations of Shenoy's expected values of utility functions.

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关于使用概率变换逼近 Shenoy 期望算子的注释

摘要 最近，Prakash P. Shenoy 引入了一种计算 Dempster-Shafer 证据理论中期望值的新方法。到目前为止，当他们需要 DS 理论中效用函数的期望值时，作者通常是间接做的：首先，他们找到与所考虑的置信函数相对应的概率测度，然后使用该概率测度计算经典概率期望. 据我们所知，Shenoy 的期望算子是第一种考虑包含在相应信念函数中的所有信息的方法。它唯一的缺点是它的指数计算复杂度。这就是为什么，在本文中，我们从角度比较了定义信念函数概率代表的五种不同方法，