  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.
