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On Random Sets Independence and Strong Independence in Evidence Theory

机译:证据理论中的随机集独立性和强独立性

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Belief and plausibility functions can be viewed as lower and upper probabilities possessing special properties. Therefore, (conditional) independence concepts from the framework of imprecise probabilities can also be applied to its sub-framework of evidence theory. In this paper we concentrate ourselves on random sets independence, which seems to be a natural concept in evidence theory, and strong independence, one of two principal concepts (together with epistemic independence) in the framework of credal sets. We show that application of strong independence to two bodies of evidence generally leads to a model which is beyond the framework of evidence theory. Nevertheless, if we add a condition on resulting focal elements, then strong independence reduces to random sets independence. Unfortunately, it is not valid no more for conditional independence.
机译:信念和合理性函数可以被视为具有特殊属性的上下概率。因此,来自不精确概率框架的(条件)独立性概念也可以应用于其证据理论的子框架。在本文中,我们将注意力集中在随机集独立性上,随机集独立性在证据理论中似乎是一个自然的概念,而强独立性则是credal集框架中两个主要概念(连同认知独立性)之一。我们表明,将强独立性应用于两个证据体系通常会导致超出证据理论框架的模型。但是,如果我们在结果焦点元素上添加条件,则强独立性会降低为随机集独立性。不幸的是,它对于条件独立不再有效。

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