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熵的多面性或贝叶斯统计力学。

Many faces of entropy or Bayesian statistical mechanics.

机构信息

Institute for Materials Science and Max Bergmann Center of Biomaterials, Dresden University of Technology, 01062 Dresden, Germany.

出版信息

Chemphyschem. 2010 Nov 15;11(16):3387-94. doi: 10.1002/cphc.201000583.

Abstract

Some 80-90 years ago, George A. Linhart, unlike A. Einstein, P. Debye, M. Planck and W. Nernst, managed to derive a very simple, but ultimately general mathematical formula for heat capacity versus temperature from fundamental thermodynamic principles, using what we would nowadays dub a "Bayesian approach to probability". Moreover, he successfully applied his result to fit the experimental data for diverse substances in their solid state over a rather broad temperature range. Nevertheless, Linhart's work was undeservedly forgotten, although it represents a valid and fresh standpoint on thermodynamics and statistical physics, which may have a significant implication for academic and applied science.

摘要

大约 80-90 年前,乔治·A·林哈特(George A. Linhart)与阿尔伯特·爱因斯坦(A. Einstein)、彼得·德拜(P. Debye)、马克斯·普朗克(M. Planck)和瓦尔特·能斯特(W. Nernst)不同,他成功地从基本热力学原理出发,利用我们现在所说的“贝叶斯概率方法”,推导出一个非常简单但最终通用的热容量与温度的数学公式。此外,他还成功地将其结果应用于在相当宽的温度范围内拟合不同物质的固态实验数据。然而,林哈特的工作却被遗忘了,尽管它代表了热力学和统计物理学的一个有效而新颖的观点,这可能对学术和应用科学有重要的意义。

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