1. 高斯概率模型：

[f,xi] = ksdensity(x): returns a probability density estimate, f, for the sample in the vector x. 
The estimate is based on a normal kernel function, and is evaluated at 100 equally spaced points, xi, that cover the range of the data in x.

代码：

>> %Give a random sample
x=[2*randn(6000,1); 5+randn(4000,1)];
%Calculate the probability density of each point
[f,xi]=ksdensity(x);
%plot
subplot(211);
plot(x);
title('Sample Data')
subplot(212);
plot(xi,f);
title('PDF');
>> 

图形：

2. 产生高斯样本点

代码：

clear all;  clc;
N=6;
s01=randi([0,1],N,1); % 01 random sequence
s=2*s01-1;    % +1-1 random sequence
w=randn(N,1); % Gaussian distribution
x=rand(N,1);  % Uniform  distribution
[s01,s,w,x]

输出：