The aim of this manuscript is to introduce the Bayesian minimum message length principle of inductive inference to a general statistical audience that may not be familiar with information theoretic statistics. We describe two key minimum message length inference approaches and demonstrate how the principle can be used to develop a new Bayesian alternative to the frequentist $t$-test as well as new approaches to hypothesis testing for the correlation coefficient. Lastly, we compare the minimum message length approach to the closely related minimum description length principle and discuss similarities and differences between both approaches to inference.
翻译:本手稿的目的是向可能不熟悉信息理论统计的一般统计对象介绍巴耶斯最低电文引言长度原则,我们描述了两种关键的最低电文推理方法,并展示如何利用该原则开发新的贝耶斯人替代经常试验美元以及相关系数假设测试的新办法。最后,我们将最低电文长度方法与密切相关的最低描述长度原则进行比较,并讨论两种推理方法之间的异同。