使数据符合正态分布 [英] Fit data to normal distribution

问题描述

我希望一些数据适合相应的高斯分布.

I want some data to fit the corresponding Gaussian distribution.

数据本来应该是高斯分布的，但是由于某些过滤的原因，它们不能完全匹配规定的和期望的高斯分布.因此，我的目标是减少数据与所需分布之间的现有分散.

The data is meant to be Gaussian already, but for some filtering reasons, they will not perfectly match the prescribed and expected Gaussian distribution. I therefore aim to reduce the existing scatter between data and desired distribution.

例如，我的数据符合高斯分布，如下所示(预期平均值为0，标准偏差为0.8):

For example, my data fit the Gaussian distribution as follows (the expected mean value is 0 and the standard deviation 0.8):

这种近似值已经很不错了，但是我真的想解决模拟数据和预期分布之间仍然明显的分散.

The approximation is already decent, but I really want to crunch the still tangible scatter between simulated data and expected distribution.

我该如何实现?

编辑

到目前为止，我已经介绍了一种安全系数，定义为:

Up to now, I have introducing kinda safety factor, defined as:

SF = expected_std/actual_std;

然后

new_data = SF*old_data;

通过这种方法，标准偏差与期望值匹配，但是根据我的理解，此过程看起来很差.

This way the standard deviation matches the expected value, but this procedure looks quite poor from my understanding.

推荐答案

如果您不想对分布进行任何非线性转换，则只需调整均值和标准差即可.

If you don't want to make any non-linear transformations of the distributions, all you can do is adjust the mean and standard deviation.

%# 1. adjust the mean (do this even if the offset is small)
data = data - mean(data);

%# 2. adjust the standard deviation
data = data/std(data) * expected_SD;

这篇关于使数据符合正态分布的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！