最大熵与贝叶斯数据分析：熵先验
Maximum Entropy and Bayesian Data Analysis: Entropic Priors
摘要：
分配客观反映实验先验信息的概率分布是使用贝叶斯方法进行数据分析的主要障碍之一。本文利用最大（相对）熵（ME）方法将已知形式的似然信息转化为贝叶斯推断的先验分布。论证的灵感和指导的直觉，从成功地使用ME方法在统计力学。对于不能重复的实验，所得到的“熵先验”在形式上与爱因斯坦涨落公式相同。然而，对于可重复的实验，可能性熵的期望值是必须包含在分析中的相关信息。高斯似然的重要情况下，详细处理。
英文摘要：
The problem of assigning probability distributions which objectively reflect the prior information available about experiments is one of the major stumbling blocks in the use of Bayesian methods of data analysis. In this paper the method of Maximum (relative) Entropy (ME) is used to translate the information contained in the known form of the likelihood into a prior distribution for Bayesian inference. The argument is inspired and guided by intuition gained from the successful use of ME methods in statistical mechanics. For experiments that cannot be repeated the resulting "entropic prior" is formally identical with the Einstein fluctuation formula. For repeatable experiments, however, the expected value of the entropy of the likelihood turns out to be relevant information that must be included in the analysis. The important case of a Gaussian likelihood is treated in detail.
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