[unifit ]函数----均匀分布的参数估计
[aht bat ]=unifit(x)
[ahat bhat,ACI,BCI ]=unifit(x)
[aht bat,ACI,BCI ]=unifit(x,ALPHA)
[normfit]函数  正态分布的参数估计
[muhat,sigmahat] = normfit(data)[muhat,sigmahat,muci,sigmaci] = normfit(data)[muhat,sigmahat,muci,sigmaci] = normfit(data,alpha)[...] = normfit(data,alpha,censoring)[...] = normfit(data,alpha,censoring,freq)[...] = normfit(data,alpha,censoring,freq,options)
Muhat(u的估计值)
sigmahat(q的估计值)
muci,sigmaci(区间)
data = normrnd(10,2,100,2);
[mu,sigma,muci,sigmaci] = normfit(data)
mu =
  10.1455  10.0527
sigma =
  1.9072  2.1256
muci =
  9.7652  9.6288
  10.5258  10.4766
sigmaci =
  1.6745  1.8663
  2.2155  2.4693
二项分布的参数估计
【 binofit】函数
phat = binofit(x,n)
[phat,pci] = binofit(x,n)
[phat,pci] = binofit(x,n,alpha)
r = binornd(100,0.6)
[phat,pci] = binofit(r,100)
r =
normrnd函数用法
    55
phat =
    0.5500
pci =
    0.4473    0.6497
[betafit]函数 –计算beta分布的参数估计
phat = betafit(data)
[phat,pci] = betafit(data,alpha)Description
data = betarnd(4,3,100,1);
[p,ci] = betafit(data,0.01)
p =
    3.9081    3.0022
ci =
    2.4864    1.8530
5.3299    4.1513
[expfit]函数—指数分布的参数估计
parmhat = expfit(data)
[parmhat,parmci] = expfit(data)
[parmhat,parmci] = expfit(data,alpha)
[...] = expfit(data,alpha,censoring)
[...] = expfit(data,alpha,censoring,freq)
data = exprnd(3, 100, 1);
[parmhat, parmci] = expfit(data, 0.01)
parmhat =
    3.0398
parmci =
    2.3817
3.9934
【poissfit】函数---泊松分布
lambdshat = poissfit(data)
[lambdahat,lambdaci] = poissfit(data)
[lambdahat,lambdaci] = poissfit(data,alpha)
r = poissrnd(5,10,2);
[l,lci] = poissfit(r)

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