How to create a pdf from the histogram

32 次查看（过去 30 天）

显示更早的评论

MechenG 2025-5-4

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2176884-how-to-create-a-pdf-from-the-histogram

评论： MechenG 2025-5-10

采纳的回答： the cyclist

matlab.mat

Hi,

My data is stored in x3 array (also attachesd here). where, x3 (1,:) - input; x3 (2,:) - output. I need to generate pdf from this data. May I know how to generate histogram and pdf from this data.

采纳的回答

the cyclist 2025-5-4

1
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2176884-how-to-create-a-pdf-from-the-histogram#answer_1564667

You can use the histogram function to create and plot a histogram.

You can use the ksdensity function to estimate a non-parametric fit to the probability density function, and then the plot function to plot it.

17 个评论
显示 15更早的评论隐藏 15更早的评论

the cyclist 2025-5-9

编辑：the cyclist 2025-5-9

在 MATLAB Online 中打开

matlab.mat

I'm not quite sure what you mean. Your data in the b vector could be anything, as they are just a sample of data that be fit by either method. The fact that they happen to be at discrete values (equally spaced or not) does not matter.

But, surely you notice that your data are not even close to normally distributed. The K-S density fit is not assuming normality, and gets the shape of the data right.

If what you meant was not about the shape, but about the scale, I'm guessing you did not scale the K-S density fit so that it matched the assumptions of histfit. I did that below, too.

% Load the data

load matlab.mat

% Non-parametric fit to the data

[f,x] = ksdensity(b);

% Calculate the normalization that will convert the KS density

% to the same scale as histfit()

dx = x(2) - x(1);

count = numel(b);

% Plot

figure

hold on

histfit(b)

plot(x,f*dx*count,"LineWidth",3)

legend(["data","histfit","ksdensity"])

the cyclist 2025-5-10

编辑：the cyclist 2025-5-10

在 MATLAB Online 中打开

matlab.mat

You need to read about and understand the method more. The KS curve is going to be incorporating data density from more than one of your discrete values.

How far left and right each point looks is determined by the kernel bandwidth. For example, look what happens when I make it very narrow:

% Load the data

load matlab.mat

% Non-parametric fit to the data

[f,x] = ksdensity(b,"BandWidth",0.3);

% Calculate the normalization that will convert the KS density

% to the same scale as histfit()

dx = x(2) - x(1);

count = numel(b);

% Plot

figure

hold on

histfit(b)

plot(x,f*dx*count,"LineWidth",2)

legend(["data","histfit","ksdensity"])

●

MechenG 2025-5-10

Ok. Now I got it. Thanks.

请先登录，再进行评论。

类别

AI and Statistics Statistics and Machine Learning Toolbox Probability Distributions and Hypothesis Tests Continuous Distributions Noncentral t Distribution

在 Help Center 和 File Exchange 中查找有关 Noncentral t Distribution 的更多信息

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by