当证据是由相对信念测量时,结合统计证据。

IF 2.1 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY
Entropy Pub Date : 2025-06-18 DOI:10.3390/e27060654
Michael Evans
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引用次数: 0

摘要

讨论了k个贝叶斯推理基中包含的关于未知的统计证据的组合问题。这可以被认为与汇集k个先验来确定共识先验的问题有关,但这里的重点是结合统计证据的度量来获得统计证据的共识度量。线性意见池被认为具有最适合这一角色的属性。特别是,线性池化保留了对证据的共识,而其他规则则没有。虽然线性池不能保持先前的独立性,但在这种情况下,它仍然表现得很好,与统计证据的表达有关。对于结合统计证据的更普遍的问题,其中先验和抽样模型可能不同,杰弗里条件化起着关键作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combining Statistical Evidence When Evidence Is Measured by Relative Belief.

The problem of combining statistical evidence concerning an unknown, contained in each of the k Bayesian inference bases, is discussed. This can be considered as being related to the problem of pooling k priors to determine a consensus prior, but the focus here is instead on combining a measure of statistical evidence to obtain a consensus measure of statistical evidence. The linear opinion pool is seen to have the most appropriate properties for this role. In particular, linear pooling preserves a consensus with respect to the evidence, and other rules do not. While linear pooling does not preserve prior independence, it is shown that it still behaves appropriately with respect to the expression of statistical evidence in such a context. For the more general problem of combining statistical evidence, where the priors as well as the sampling models may differ, Jeffrey conditionalization plays a key role.

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来源期刊
Entropy
Entropy PHYSICS, MULTIDISCIPLINARY-
CiteScore
4.90
自引率
11.10%
发文量
1580
审稿时长
21.05 days
期刊介绍: Entropy (ISSN 1099-4300), an international and interdisciplinary journal of entropy and information studies, publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish as much as possible their theoretical and experimental details. There is no restriction on the length of the papers. If there are computation and the experiment, the details must be provided so that the results can be reproduced.
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