用正态分布拟合加权直方图 [英] Fitting a weighted histogram with a normal distribution

问题描述

我知道如何使用SCipy库拟合以正态分布输入直方图的数据(用python拟合直方图)，但是如果在具有数据的基础上又具有相同维度的权重数组，我该怎么做?有适当的功能吗?还是我应该创建一个由数据提供数据并对其加权的第二个数组?

I know how to fit the data entering an histogram with a normal distribution using the SCipy library (Fitting a histogram with python) but how could I do the same if on top of having data I have an array of weights having the same dimension. Is there a proper function for that or should I create a second array fed by the data and weighting it myself?

干杯.

这已经在这里得到了答案:

This is pretty much already answered here:

NumPy中的加权标准偏差?

推荐答案

如果您正在寻找正态分布N(mu，sigma) 您可以从输入数据中精确计算出mu和sigma.

If you are looking for a Normal distribution N(mu, sigma) you can calculate exactly mu and sigma from the input data.

例如:X = x1，...，xN是值，W = w1，...，wN是权重

For example: X = x1,...,xN are the values and W = w1,..., wN their weights

mu = sum (X * W) / sum(W)
sigma = np.sqrt (sum (W * (X- mu)**2) / sum(W))

如果您要使用另一种分布，我建议了一个答案

If you are to fit another kind of distribution, I suggested an answer here using OpenTURNS library.

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