Compute the cdf values for a normal distribution by specifying the distribution name'Normal'and the distribution parameters.

Define the input vectorxto contain the values at which to calculate the cdf.

x = [-2,-1,0,1,2];

计算平均值的正态分布的CDF值 $$\mu$$等于1和标准偏差 $$\sigma$$等于5。

mu = 1; sigma = 5; y = cdf('Normal',x,mu,sigma)

y =1×50.2743 0.3446 0.4207 0.5000 0.5793

每个值INycorresponds to a value in the input vectorx. For example, at the valuexequal to 1, the corresponding cdf valueyis equal to 0.5000.

创建正态分布对象，并使用对象计算正态分布的CDF值。

Create a normal distribution object with the mean $$\mu$$等于1和标准偏差 $$\sigma$$等于5。

mu = 1; sigma = 5; pd = makedist('Normal','mu'，亩，'sigma'，Sigma）;

Define the input vectorxto contain the values at which to calculate the cdf.

x = [-2,-1,0,1,2];

Compute the cdf values for the normal distribution at the values inx.

y = cdf（pd，x）

y =1×50.2743 0.3446 0.4207 0.5000 0.5793

每个值INycorresponds to a value in the input vectorx. For example, at the valuexequal to 1, the corresponding cdf valueyis equal to 0.5000.

Create a Poisson distribution object with the rate parameter, $$\lambda$$, equal to 2.

lambda = 2;PD = Makedist（“泊松”,'lambda'，lambda）;

Define the input vectorxto contain the values at which to calculate the cdf.

x = [0,1,2,3,4];

Compute the cdf values for the Poisson distribution at the values inx.

y = cdf（pd，x）

y =1×50.1353 0.4060 0.6767 0.8571 0.9473

每个值INycorresponds to a value in the input vectorx. For example, at the valuexequal to 3, the corresponding cdf valueyis equal to 0.8571.

另外，您可以计算相同的CDF值，而无需创建概率分发对象。使用cdffunction, and specify a Poisson distribution using the same value for the rate parameter, $$\lambda$$.

y2 = cdf(“泊松”,x,lambda)

y2 =1×50.1353 0.4060 0.6767 0.8571 0.9473

CDF值与使用概率分布对象计算的值相同。

创建标准的正态分布对象。

PD = Makedist（'Normal')

pd = NormalDistribution Normal distribution mu = 0 sigma = 1

Specify thexvalues and compute the cdf.

x = -3:.1:3; p = cdf(pd,x);

Plot the cdf of the standard normal distribution.

plot(x,p)

创建三个伽马分配对象。第一个使用默认参数值。第二个指定a = 1andb = 2. The third specifiesa = 2andb = 1.

pd_gamma = makedist('Gamma')

pd_gamma = GammaDistribution Gamma distribution a = 1 b = 1

pd_12 = makedist('Gamma','a'，1，'b'，2）

pd_12 = GammaDistribution Gamma distribution a = 1 b = 2

pd_21 = makedist('Gamma','a'，2，'b'，1）

pd_21 = GammaDistribution Gamma distribution a = 2 b = 1

Specify thexvalues and compute the cdf for each distribution.

x = 0：.1：5;cdf_gamma = cdf（pd_gamma，x）;CDF_12 = CDF（PD_12，X）;CDF_21 = CDF（PD_21，X）;

Create a plot to visualize how the cdf of the gamma distribution changes when you specify different values for the shape parametersaandb.

figure; J = plot(x,cdf_gamma); holdon;k =图（x，cdf_12，'r-'); L = plot(x,cdf_21,'k-.'); set(J,'行宽'，2）;set（k，'行宽'，2）;传奇（[J k l]，'a = 1, b = 1','a = 1, b = 2','a = 2, b = 1','Location','东南'); holdoff;

Fit Pareto tails to a $$t$$distribution at cumulative probabilities 0.1 and 0.9.

t = trnd(3,100,1); obj = paretotails(t,0.1,0.9); [p,q] = boundary(obj)

p =2×10.1000 0.9000

q =2×1-1.8487 2.0766

Compute the cdf at the values inq.

cdf(obj,q)

ans =2×10.1000 0.9000