Python – 统计中的正态逆高斯分布

scipy.stats.norminvgauss()是一个正态逆高斯连续随机变量。它作为rv_continuous 类的实例继承自泛型方法。它使用特定于此特定发行版的详细信息来完成方法。

参数 ：

q : lower and upper tail probability
x : quantiles
loc : [optional]location parameter. Default = 0
scale : [optional]scale parameter. Default = 1
size : [tuple of ints, optional] shape or random variates.
moments : [optional] composed of letters [‘mvsk’]; ‘m’ = mean, ‘v’ = variance, ‘s’ = Fisher’s skew and ‘k’ = Fisher’s kurtosis. (default = ‘mv’).

Results : Normal Inverse Gaussian continuous random variable

代码#1：创建正态逆高斯连续随机变量

# importing library
  
from scipy.stats import norminvgauss
    
numargs = norminvgauss.numargs 
a, b = 4.32, 3.18
rv = norminvgauss(a, b) 
    
print ("RV : \n", rv)  

输出 ：

RV : 
 scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D7E7F988

代码#2：正态逆高斯连续变量和概率分布

import numpy as np 
quantile = np.arange (0.01, 1, 0.1) 
  
# Random Variates 
R = norminvgauss.rvs(a, b) 
print ("Random Variates : \n", R) 
  
# PDF 
R = norminvgauss.pdf(a, b, quantile) 
print ("\nProbability Distribution : \n", R) 

输出 ：

Random Variates : 
 1.3435537740460517

Probability Distribution : 
 [1.47553069e-06 2.26852616e-06 3.47672896e-06 5.31156917e-06
 8.08889275e-06 1.22787583e-05 1.85780134e-05 2.80155365e-05
 4.21040186e-05 6.30575858e-05]
 

代码#3：图形表示。

import numpy as np 
import matplotlib.pyplot as plt 
     
distribution = np.linspace(0, np.minimum(rv.dist.b, 3)) 
print("Distribution : \n", distribution) 
     
plot = plt.plot(distribution, rv.pdf(distribution)) 

输出 ：

Distribution : 
 [0.         0.04081633 0.08163265 0.12244898 0.16326531 0.20408163
 0.24489796 0.28571429 0.32653061 0.36734694 0.40816327 0.44897959
 0.48979592 0.53061224 0.57142857 0.6122449  0.65306122 0.69387755
 0.73469388 0.7755102  0.81632653 0.85714286 0.89795918 0.93877551
 0.97959184 1.02040816 1.06122449 1.10204082 1.14285714 1.18367347
 1.2244898  1.26530612 1.30612245 1.34693878 1.3877551  1.42857143
 1.46938776 1.51020408 1.55102041 1.59183673 1.63265306 1.67346939
 1.71428571 1.75510204 1.79591837 1.83673469 1.87755102 1.91836735
 1.95918367 2.        ]