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Python – Reciprocal Inverse Gaussian Distribution in Statistics

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scipy.stats.recipinvgauss() is a reciprocal inverse Gaussian continuous random variable. It is inherited from the of generic methods as an instance of the rv_continuous class. It completes the methods with details specific for this particular distribution. Parameters :
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 : reciprocal inverse Gaussian continuous random variable
Code #1 : Creating reciprocal inverse Gaussian continuous random variable
# importing library
  
from scipy.stats import recipinvgauss   
    
numargs = recipinvgauss   .numargs 
a, b = 4.32, 3.18
rv = recipinvgauss   (a, b) 
    
print ("RV : \n", rv) 

                    
Output :
RV : 
 scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D843A9C8
Code #2 : reciprocal inverse Gaussian continuous variates and probability distribution
import numpy as np 
quantile = np.arange (0.01, 1, 0.1
  
# Random Variates 
R = recipinvgauss  .rvs(a, b) 
print ("Random Variates : \n", R) 
  
# PDF 
R = recipinvgauss  .pdf(a, b, quantile) 
print ("\nProbability Distribution : \n", R)  

                    
Output :
Random Variates : 
 6.540700180076524

Probability Distribution : 
 [0.03015471 0.03206632 0.03410829 0.03629051 0.03862377 0.04111981
 0.04379146 0.04665275 0.04971902 0.05300712]

Code #3 : Graphical Representation.
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)) 

                    
Output :
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.        ]
  
Code #4 : Varying Positional Arguments
import matplotlib.pyplot as plt 
import numpy as np 
     
x = np.linspace(0, 5, 100
     
# Varying positional arguments 
y1 = recipinvgauss   .pdf(x, 1, 3, 5
y2 = recipinvgauss   .pdf(x, 1, 4, 4
plt.plot(x, y1, "*", x, y2, "r--"

                    
Output :

Last Updated : 14 Jan, 2020
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