The Principle of Maximum Entropy is a rigorous technique for estimating an unknown distribution given partial information while simultaneously minimizing bias. However, an important requirement for applying the principle is that the available information be provided error-free (Jaynes 1982). We relax this requirement using a memoryless communication channel as a framework to derive a new, more general principle. We show our new principle provides an upper bound on the entropy of the unknown distribution and the amount of information lost due to the use of a given communications channel is unknown unless the unknown distribution's entropy is also known. Using our new principle we provide a new interpretation of the classic principle and experimentally show its performance relative to the classic principle and other generally applicable solutions. Finally, we present a simple algorithm for solving our new principle and an approximation useful when samples are limited.

翻译：最大熵原理是一种在给定部分信息的同时最小化偏差以估计未知分布的严谨技术。然而，应用该原理的一个重要前提是可用信息必须无误差地提供（Jaynes 1982）。我们通过使用无记忆通信信道作为框架来放宽这一要求，从而推导出一个新的、更普遍的原理。我们证明，新原理为未知分布的熵提供了一个上界，并且由于使用给定通信信道而导致的信息损失量是未知的，除非未知分布的熵也已知。利用新原理，我们对经典原理提出了新的解释，并通过实验展示了其相对于经典原理及其他通用解决方案的性能。最后，我们提出了一种求解新原理的简单算法，以及一种在样本有限时有用的近似方法。