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Bayesian Inference under Ambiguity: Conditional Prior Belief Functions

机译:模糊性下的贝叶斯推断:条件先验信念函数

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Bayesian inference under imprecise prior information is studied: the starting point is a precise strategy $σ$ and a full B-conditional prior belief function $Bel_B$, conveying ambiguity in probabilistic prior information. In finite spaces, we give a closed form expression for the lower envelope $underline{P}$ of the class of full conditional probabilities dominating $(Bel_B,σ)$ and, in particular, for the related “posterior probabilities”. The assessment $(Bel_B,σ)$ is a coherent lower conditional probability in the sense of Williams and the characterized lower envelope $underline{P}$ coincides with its natural extension.
机译:研究了不精确先验信息下的贝叶斯推理:起点是精确策略$σ$和完整的B条件先验置信函数$ Bel_B $,传达了概率先验信息中的歧义。在有限空间中,我们给出以全条件概率类别为$(Bel_B,σ)$为主的下信封$ underline {P} $的闭式表达式,尤其是相关的“后验概率”。在Williams的意义上,评估$(Bel_B,σ)$是一个连贯的较低条件概率,其特征在于较低的包络线下划线{P} $与它的自然扩展一致。

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