A channel-based perspective on conjugate priors
A channel-based perspective on conjugate priors
复制标题
基于通道的共轭先验视角
DOI：
10.1017/s0960129519000082
复制
发表时间：
2017
影响因子：
0.5
通讯作者：
B. Jacobs
中科院分区：
文献类型：
--
作者：
B. Jacobs
在贝叶斯概率中，一个理想的闭包特性是更新后验分布与先验分布在同一类分布中，比如高斯分布。当通过统计模型进行更新时，人们将这类先验分布称为模型的“共轭先验”。本文给出了(1)用图形语言用通道对共轭先验概念的抽象表述；(2)对共轭先验产生贝叶斯反转的简单抽象证明；(3)对多次更新的推广。用几个标准的例子说明了这一理论。
Abstract A desired closure property in Bayesian probability is that an updated posterior distribution be in the same class of distributions – say Gaussians – as the prior distribution. When the updating takes place via a statistical model, one calls the class of prior distributions the ‘conjugate priors’ of the model. This paper gives (1) an abstract formulation of this notion of conjugate prior, using channels, in a graphical language, (2) a simple abstract proof that such conjugate priors yield Bayesian inversions and (3) an extension to multiple updates. The theory is illustrated with several standard examples.
影响因子：
2.4
作者：
José M Bernardo and Adrian F M Smith-José-M-Bernardo-and-Adrian-F-M-Smith-2177748935
通讯作者：
José M Bernardo and Adrian F M Smith-José-M-Bernardo-and-Adrian-F-M-Smith-2177748935