抽样的技术上机实验中心极限定理验证.docxVIP

均匀分布中心极限定律的实现： clc clear n=200000; %/* ???′′?êy*/ k=100; %/* ?ù±???êy*/ mu=0; u=0; sigma=1/12; population=0:0.001:1; for i=1:n y = randsample(population,k,1); mu=[mu,mean(y)]; end mu=(mu-0.5)/(sqrt(sigma)/sqrt(k)); %hist(mu(2:end),1000) [f, x1] = ksdensity(mu(2:end)); plot(x1, f) hold on plot(x1,normpdf(x1,0,1),r) hold off %%%%%%%%%%%%%%%%%%%%%%%% 两点分布的实现： clc clear n=10000; %/* ???′′?êy*/ k=100; %/* ?ù±???êy*/ mu=0; u=0; p=0.5; sigma=p*(1-p); population=0:1; for i=1:n y = randsample(population,k,1); mu=[mu,mean(y)]; end mu=(mu-p)/(sqrt(sigma)/sqrt(k)); %hist(mu(2:end),1000) [f, x1] = ksdensity(mu(2:end)); plot(x1, f) hold on plot(x1,normpdf(x1,0,1),r) hold off %%%%%%%%%%%%%%%%%%%%%%%%%%%%% 两点分布1以概率0.4发生 clc clear n=50000; %/* ????????*/ k=100; %/* ?¨′?à?????*/ mu=0; u=0; p=0.4; sigma=p*(1-p); a=0:0.1:1; for i=1:n y = randsample(a(2:end)=p,k,1); mu=[mu,mean(y)]; end mu=(mu-p)/(sqrt(sigma)/sqrt(k)); hist(mu(2:end),1000) [f, x1] = ksdensity(mu(2:end)); plot(x1, f) hold on plot(x1,normpdf(x1,0,1),r) hold off %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 二项分布随机变量的中心极限定理检验： clc clear n=50000; %/* ????????*/ k=100; %/* ?¨′?à?????*/ bino=0; u=0; p=0.4; sigma=p*(1-p); a=0:0.1:1; for i=1:n y = randsample(a(2:end)=p,k,1); bino=[bino,sum(y)] ; end bino=(bino-k*p)/sqrt((k*sigma)); hist(bino(2:end),n/10) [f, x1] = ksdensity(bino(2:end)); plot(x1, f) hold on plot(x1,normpdf(x1,0,1),r) hold off %%%%%%%%%%%%%%%%%%%%%%%%%%%% 卡方分布： clc clear n=10000; %/* ???′′?êy*/ m=500; v=5;%×?óé?è R= chi2rnd(v,n,m);%%%可以换成以下任何一种分布； r=sum(R)/m;%????′?êμ?é??áDè?oí mu=(r-v)*sqrt(m)./sqrt(2*v); [f, x1] = ksdensity(mu(2:end)); plot(x1, f) hold on plot(x1,normpdf(x1,0,1),r) hold off （一）Matlab内部函数 a.?基本随机数 Matlab中有两个最基本生成随机数的函数。 1．rand() 生成（0,1）区间上均匀分布的随机变量。基本语法： rand([M,N,P ...]) 生成排列成M*N*P... 多维向量的随机数。如果只写M，则生成M*M矩阵；如果参数为[M,N]可以省略掉方括号。一些例子： rand(5,1) %??成5个随机数排列的列向量，一般用这种格式 rand(5) %生成5行5列的随机数矩阵 rand([5,4]) %生成一个5行4列的随机数矩阵 生成的随机