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信息论中各种熵的matlab实现，其中包括自信息量，互信息量，条件熵，联合熵，冗余度等等的计算。-various information theory entropy Matlab achieved, which include the volume of information, mutual information, the conditional entropy, Joint entropy, and so on the redundancy terms.
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贝叶斯（Bayesion）机器学习相互信息。用于求取熵、联合熵等。改进后可用于随机反演-Bayesian (Bayesion) machine learning of mutual information. Used to obtain entropy, joint entropy and so on. Improved can be used for stochastic inversion
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熵,联合熵,条件熵,平均互信息量的通用计算程序-Entropy, joint entropy, conditional entropy, average mutual information of the general-purpose computer program
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实现联合熵算法并能够在Matlab平台上编写出程序，用联合熵算法分析医学信号并得出结果-Achieve joint entropy algorithm in Matlab platform and be able to write a program, with joint entropy algorithm analysis and the outcome of medical signal
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进行文章里的字母实验，统计了文章中的26个英文字母的熵，联合熵和条件熵。利用matlab的统计工具进行了联合熵和条件熵的简单计算，得出26个字母在《圣经》中的概率分布直方图。
关键词：熵；信息量；统计理论;联合概率；条件概率(Carry on the letter experiment in the article, count the entropy of the 26 English letters in the article, combine entropy and condition
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joint entropy of x y variables
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实现互信息程序化，对于互信息熵计算还有联合熵计算可以实现，不用手动计算，方便快捷。(To achieve mutual information programming, for mutual information entropy calculation and joint entropy calculation can be achieved, without manual calculation, convenient and quick.)
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conditional entropy, entropy, joint entropy, mutual information, relative entropy basic features are implemented in matlab approach
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This is an implementation of Mutual Information theory in Matlab. This code consists of three parts including: Entropy, Joint Entropy and Mutual Information matlab codes.
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本程序适用于matlab中，读取两幅图像，求取两幅图像的联合熵
程序非常易懂，适合初学者使用，可以用里面的图片进行测试，注意对程序读取图片名进行更改。(Read the two images and calculate the joint entropy of the two images.)
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图像融合中的平均梯度、相关系数、信息熵、交叉熵、联合熵、均方误差、互信息、信噪比、峰值信噪比、均方根误差、空间频率、标准差、均值、扭曲程度、偏差指数等等(Average gradient, correlation coefficient, information entropy, cross-entropy, joint entropy, mean square error, mutual information, signal-to-noise ratio, peak signal-to-no
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此代码为计算离散随机变量的熵、联合熵、条件熵、互信息的 Matlab 程序(This code is a matlab program for calculating entropy, joint entropy, conditional entropy and mutual information of discrete random variables)
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