如何从正态概率密度函数中提取拟合数据 [英] How to extract fitted data from normal probability density function

问题描述

如果我用正态分布拟合单变量数据，如何在MATLAB中取回拟合值. 我正在使用这个简单的例子

If I fit a uni-variate data with normal distribution, how can i get back the fitted values in MATLAB. I am using this simple example

load hospital % data
x = hospital.Weight;
[mu sigma]=normfit(x) %normal fitting
%To visualize the pdf
xval=min(x):0.1:max(x)
yval=normpdf(xval,mu,sigma)
plot(xval,yval)

yval给出xval值的概率.现在，如果我想用上述正态分布近似提取出"x"的拟合值，该怎么做?从图片中可以看出，y轴值为pdf，位于0和1之间，但是我希望从遵循正态分布的数据中获得相应的拟合值.

yval is giving the probabilities of xval values. Now, If I would like to extract the fitted values of 'x' after approximating it with the above normal distribution, how do I do that?. As can be seen in the picture the y-axis values are the pdf and lies between 0 and 1, however I want the corresponding fitted values from the data that follows normal distribution.

拟合值是否为x_fitted = yval * sigma + mu?我想我这里缺少一些基本的数学知识.

Would the fitted values be x_fitted = yval*sigma + mu? !I think I am missing some basic maths here.

推荐答案

normfit只是为您提供了适合的普通pdf的mu和sigma.从这些文件中，您可以使用normpdf构建该pdf.因此，输入x所需的y值应为

normfit simply gives you the mu and sigma of the fitted normal pdf. From those you build that pdf with normpdf. So the desired y values for your input x would be

y = normpdf(x,mu,sigma)

您可以用它绘制的

hold on
plot(x,y,'ro')

请注意，通过此过程，即使数据实际上未遵循正态分布，数据也恰好位于正态pdf上.

Note that, with this procedure, the data lie exactly on the normal pdf, even if those data do not actually follow a normal distribution.

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