原
期望、方差、协方差和协方差矩阵
1.离散随机变量的X的数学期望：##
ρXY=0, 两个变量不相关
四、协方差矩阵
推广到多维：
对于连续的情况：
例子：
可以参考下面的博客：
详解协方差与协方差矩阵：https://blog.csdn.net/ybdesire/article/details/6270328
参考： 概率论与数理统计 浙大
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<a href="https://blog.csdn.net/liuweiyuxiang/article/details/77566342" target="_blank" title="机器学习中的数学(3)——协方差矩阵和散布（散度）矩阵">
<span class="desc oneline">1、引言在学习机器学习算法和阅读相关论文的时候，将经常会看到协方差矩阵和散布矩阵的身影，这说明它们在机器学习中具有很重要的作用，究竟有什么样的作用，下面我们就做简要的介绍和分析。2、统计学上的基本概念...</span>
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numpy求<em>协方差</em>矩阵（numpy.cov()） </h4>
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<span class="date hover-show">12-02</span>
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<a href="https://blog.csdn.net/u013521296/article/details/84714310" target="_blank" title="numpy求协方差矩阵（numpy.cov()）">
<span class="desc oneline">1、关于方差与协方差方差公式：方差度量协方差公式：协方差矩阵的介绍和计算见：https://blog.csdn.net/Mr_HHH/article/details/784905762、numpy.c...</span>
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<em>期望</em>，<em>方差</em>，<em>协方差</em>，标准差，<em>协方差</em>矩阵 </h4>
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<span class="desc oneline">一些公式会用到的函数期望：mean方差:var协方差：cov标准差：std相关系数：暂时没找到具体的各个函数用法见链接补充的说明：协方差矩阵计算的是不同维度之间的协方差，而不是不同样本之间的。拿到一个...</span>
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<span class="desc oneline">期望一件事情有n种结果，每一种结果值为xixix_i，发生的概率记为pipip_i，那么该事件发生的期望为：E=∑i=1nxipiE=∑i=1nxipiE=\sum_{i=1}^{n}{x_i}{p_...</span>
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<span class="desc oneline">向量随机变量X的数学期望也是一个向量，其各分量是原X的各个分量的数学期望。如果f(x)是d维随机变量X的n维向量函数 ...</span>
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