scipy.stats.mielke() is a Mielke Beta-Kappa / Dagum 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 : Mielke continuous random variable
Code #1 : Creating Mielke continuous random variable
# importing library from scipy.stats import mielke numargs = mielke.numargs a, b = 4.32, 3.18rv = mielke(a, b) print ("RV : \n", rv) |
Output :
RV : scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D6DD04C8
Code #2 : Mielke continuous variates and probability distribution
import numpy as np quantile = np.arange (0.01, 1, 0.1) # Random Variates R = mielke.rvs(a, b) print ("Random Variates : \n", R) # PDF R = mielke.pdf(a, b, quantile) print ("\nProbability Distribution : \n", R) |
Output :
Random Variates : 0.541154909484041 Probability Distribution : [6.94878143e-96 6.26408333e-09 7.39143540e-05 1.84143774e-03 8.76316044e-03 2.10584824e-02 3.57237873e-02 4.95347163e-02 6.04795424e-02 6.78033254e-02]
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 = mielke .pdf(x, 1, 3) y2 = mielke .pdf(x, 1, 4) plt.plot(x, y1, "*", x, y2, "r--") |
Output :
