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→信息论 Information theory
负熵通常用于统计和信号处理。它与网络熵有关，网络熵常用于<font color="#ff8000">进行独立成分分析 independent component analysis</font>。<ref>P. Comon, Independent Component Analysis – a new concept?, ''Signal Processing'', '''36''' 287–314, 1994.</ref><ref>Didier G. Leibovici and Christian Beckmann, [http://www.fmrib.ox.ac.uk/analysis/techrep/tr01dl1/tr01dl1/tr01dl1.html An introduction to Multiway Methods for Multi-Subject fMRI experiment], FMRIB Technical Report 2001, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Department of Clinical Neurology, University of Oxford, John Radcliffe Hospital, Headley Way, Headington, Oxford, UK.</ref>
负熵通常用于统计和信号处理。它与网络熵有关，网络熵常用于'''<font color="#ff8000">独立成分分析 independent component analysis</font>'''。<ref>P. Comon, Independent Component Analysis – a new concept?, ''Signal Processing'', '''36''' 287–314, 1994.</ref><ref>Didier G. Leibovici and Christian Beckmann, [http://www.fmrib.ox.ac.uk/analysis/techrep/tr01dl1/tr01dl1/tr01dl1.html An introduction to Multiway Methods for Multi-Subject fMRI experiment], FMRIB Technical Report 2001, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Department of Clinical Neurology, University of Oxford, John Radcliffe Hospital, Headley Way, Headington, Oxford, UK.</ref>
一个分布的负熵等于 <math>p_x</math> 和具有与 <math>p_x</math> 相同均值和方差的正态分布的 Kullback-Leibler 散度(参见正态分布的<font color="#ff8000">微分熵 Differential entropy</font>和最大化)。特别地，负熵总是非负的。
一个分布的负熵等于 <math>p_x</math> 和具有与 <math>p_x</math> 相同均值和方差的正态分布的 Kullback-Leibler 散度（参见正态分布的<'''font color="#ff8000">微分熵 Differential entropy</font>'''和最大化）。特别地，负熵总是非负的。
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==统计学负熵与吉布斯自由能的关联 Correlation between statistical negentropy and Gibbs' free energy ==
==统计学负熵与吉布斯自由能的关联 Correlation between statistical negentropy and Gibbs' free energy ==