熵或贝叶斯统计力学的多面性
Many Faces of Entropy or Bayesian Statistical Mechanics
摘要：
大约80-90年前，乔治·A. Linhart不像A.爱因斯坦，P.德拜，M. Planck和W. Nernst，已经设法从基本的热力学原理中推导出一个非常简单但最终通用的热容与温度的数学公式，使用我们现在称之为“贝叶斯概率方法”。此外，他还成功地应用他的结果来拟合不同物质在相当宽的温度范围内的固态实验数据。尽管如此，林哈特的工作被不应该地遗忘了，尽管它确实代表了热力学和统计物理学的有效和新鲜的观点，这可能对学术和应用科学有重要意义。
英文摘要：
Some 80-90 years ago, George A. Linhart, unlike A. Einstein, P. Debye, M. Planck and W. Nernst, has managed to derive a very simple, but ultimately general mathematical formula for heat capacity vs. temperature from the fundamental thermodynamical principles, using what we would nowadays dub a "Bayesian approach to probability". Moreover, he has successfully applied his result to fit the experimental data for diverse substances in their solid state in a rather broad temperature range. Nevertheless, Linhart's work was undeservedly forgotten, although it does represent a valid and fresh standpoint on thermodynamics and statistical physics, which may have a significant implication for academic and applied science.
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