多模型判别的贝叶斯优化设计准则

F. Z. Labbaf, H. Talebi
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引用次数: 0

摘要

. 本文考虑了如何在几种竞争模型中求得最优设计的问题。我们使用贝叶斯方法给出了一个最优性准则。这是将贝叶斯kl -最优性扩展到两个以上的模型。修改是为了处理嵌套模型。提出的贝叶斯最优性准则是一个加权平均,其中权重是模型的相应概率,使它们为真。我们认为这些概率来自泊松分布。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian Optimum Design Criterion for Multi Models Discrimination
. The problem of obtaining the optimum design, which is able to discriminate between several rival models has been considered in this paper. We give an optimality-criterion, using a Bayesian approach. This is an exten-sion of the Bayesian KL-optimality to more than two models. A modification is made to deal with nested models. The proposed Bayesian optimality criterion is a weighted average, where the weights are corresponding probabilities of models to let them be true. We consider these probabilities coming from a Poisson distribution.
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