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Convergences of Random Variables with Respect to Coherent Upper Probabilities Defined by Hausdorff Outer Measures

机译:关于Hausdorff外部测度定义的相干上概率的随机变量的收敛

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Given a non-empty set Ω and a partition B of Ω let L be the class of all subsets of Ω. Upper conditional probabilities P(AB) are defined on L × B by a class of Hausdorff outer measures when the conditioning event B has positive and finite Hausdorff measure in its dimension; otherwise they are defined by a 0-1 valued finitely additive (but not countably additive) probability. The unconditional upper probability is obtained as a particular case when the conditioning event is Ω. Relations among different types of convergence of sequences of random variables are investigated with respect to this upper probability. If Ω has finite and positive Hausdorff outer measure in its dimension the given upper probability is continuous from above on the Borel σ-field. In this case we obtain that the pointwise convergence implies the ji-stochastic convergence. Moreover, since the outer measure is subadditive then stochastic convergence with respect to the given upper probability implies convergence in μ-distribution.
机译:给定一个非空集Ω和Ω的分区B,令L为Ω所有子集的类别。当条件事件B在维度上具有正和有限的Hausdorff测度时,通过一类Hausdorff外部测度在L×B上定义较高的条件概率P(AB);否则,它们是由0-1值的有限加法(但不可加法)概率定义的。作为特殊情况,当条件事件为Ω时,将获得无条件上限概率。关于该较高概率,研究了随机变量序列的不同收敛类型之间的关系。如果Ω在其维数上具有有限且正的Hausdorff外部度量,则在Borelσ场上,给定的较高概率从上方连续。在这种情况下,我们得到了逐点收敛意味着ji随机收敛。此外,由于外部测度是次加性的,因此相对于给定的较高概率,随机收敛表示μ分布收敛。

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