使用Kendall的双变量高斯copula从β分布中生成与随机数据相关的随机数据taurank correlation equal to -0.5.

计算线性些小n parameter from the rank correlation value.

RNGdefault％可再现性tau = -0.5;rho = copulaparam（“高斯”,tau)

Rho = -0.7071

Use a Gaussian copula to generate a two-column matrix of dependent random values.

u = copularnd('gaussian'，Rho，100）;

Each column contains 100 random values between 0 and 1, inclusive, sampled from a continuous uniform distribution.

Create ascatterhistplot to visualize the random numbers generated using the copula.

figure scatterhist(u(:,1),u(:,2))

直方图表明，副群的每一列中的数据具有边缘均匀分布。散点图显示两列中的数据是负相关的。

Use the inverse cdf functionbetainvto transform each column of the uniform marginal distributions into random numbers from a beta distribution. In the first column, the first shape parameterAis equal to 1, and a second shape parameterB等于2。In the second column, the first shape parameterA等于1.5，第二个形状参数B等于2。

b = [betainv（u（：：，1），1,2），betainv（u（：：，2），1.5,2）];

Create ascatterhist图可视化相关的beta分布数据。

图散射史（b（：：，1），b（：，2））

直方图显示了每个变量的边缘β分布。散点图显示负相关。

Verify that the sample has a rank correlation approximately equal to the initial value for Kendall'stau.

tau_sample = corr（b，'类型',“肯德尔”)

tau_sample =2×21.0000 -0.5135 -0.5135 1.0000

The sample rank correlation of -0.5135 is approximately equal to the -0.5 initial value fortau.