Conditional maximum-entropy method for selecting prior distributions in Bayesian statistics
Conditional maximum-entropy method for selecting prior distributions in Bayesian statistics
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DOI：
10.1209/0295-5075/108/40008
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发表时间：
2014-11-01
期刊：
影响因子：
1.8
通讯作者：
Abe, Sumiyoshi
中科院分区：
文献类型：
--
作者：
Abe, Sumiyoshi
为了在贝叶斯统计中选择先验概率分布进行参数估计，提出了条件最大熵法（C-MaxEnt）。该方法的灵感来自于对具有大量分离时间尺度的动力学控制的系统的统计力学方法，并基于三个关键概念：变量的共轭对，具有粗粒度因素的无因次积分措施和联合熵的部分最大化。该方法使人们能够以一种简单的方式纯粹从似然计算先验。特别是，它不仅显示了杰弗里斯的规则，而且揭示了隐藏在这些规则背后的新结构。中国人民解放军版权所有
The conditional maximum-entropy method (abbreviated here as C-MaxEnt) is formulated for selecting prior probability distributions in Bayesian statistics for parameter estimation. This method is inspired by a statistical-mechanical approach to systems governed by dynamics with largely separated time scales and is based on three key concepts: conjugate pairs of variables, dimensionless integration measures with coarse-graining factors and partial maximization of the joint entropy. The method enables one to calculate a prior purely from a likelihood in a simple way. It is shown, in particular, how it not only yields Jeffreys's rules but also reveals new structures hidden behind them. Copyright (C) EPLA, 2014