论文标题
布莱恩的最大熵方法 - 诊断有缺陷的论点及其补救措施
Bryan's Maximum Entropy Method -- diagnosis of a flawed argument and its remedy
论文作者
论文摘要
最大熵方法（MEM）是一种基于贝叶斯推论的流行数据分析技术，该技术在研究文献中发现了各种应用。尽管MEM本身在统计数据中占据了良好的依据，但我认为它最初由Bryan提出的最先进的实施将人为地限制了其解决方案空间。这种限制会导致在当代MEM研究中通常无法说明系统错误。本文的目的是仔细重新审视布莱恩的思想列车，指出其在将线性代数论证应用于固有的非线性问题上的缺陷，并提出可能克服它的可能方法。
The Maximum Entropy Method (MEM) is a popular data analysis technique based on Bayesian inference, which has found various applications in the research literature. While the MEM itself is well-grounded in statistics, I argue that its state-of-the-art implementation, suggested originally by Bryan, artificially restricts its solution space. This restriction leads to a systematic error often unaccounted for in contemporary MEM studies. The goal of this paper is to carefully revisit Bryan's train of thought, point out its flaw in applying linear algebra arguments to an inherently nonlinear problem, and suggest possible ways to overcome it.
