论文标题
最小消息长度推理简介
Introduction to minimum message length inference
论文作者
论文摘要
本手稿的目的是向可能不熟悉信息理论统计数据的一般统计受众介绍贝叶斯最小信息长度原则。我们描述了两种关键的最小消息长度推理方法，并演示了如何使用该原理来开发新的贝叶斯替代方案，以实现频繁的$ t $检验以及新方法进行相关系数的假设测试。最后，我们将最小消息长度方法与紧密相关的最小描述长度原理进行比较，并讨论两种推论方法之间的相似性和差异。
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.
