scipy.stats.betaprime() is an beta prime continuous random variable that is defined with a standard format and some shape parameters to complete its specification.
Parameters :
q : lower and upper tail probability
a, b : shape parameters
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 : beta prime continuous random variable
Code #1 : Creating betaprime continuous random variable
# importing scipy from scipy.stats import betaprime numargs = betaprimeprime.numargs [a, b] = [0.6, ] * numargs rv = betaprimeprime(a, b) print ("RV : \n", rv) |
Output :
RV : <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000029482FCC438>
Code #2 : betaprime random variates and probability distribution.
import numpy as np quantile = np.arange (0.01, 1, 0.1) # Random Variates R = betaprime.rvs(a, b, scale = 2, size = 10) print ("Random Variates : \n", R) # PDF R = betaprime.pdf(quantile, a, b, loc = 0, scale = 1) print ("\nProbability Distribution : \n", R) |
Output :
Random Variates : [ 1.59603917 1.92408727 1.2120992 0.34064091 2.68681773 22.99956678 1.45523032 2.93360219 23.93717261 18.04203815] Probability Distribution : [2.58128122 0.8832351 0.61488062 0.47835546 0.39160163 0.33053737 0.28490363 0.24941484 0.22101038 0.1977718 ]
Code #3 : Graphical Representation.
import numpy as np import matplotlib.pyplot as plt distribution = np.linspace(0, np.minimum(rv.dist.b, 5)) print("Distribution : \n", distribution) plot = plt.plot(distribution, rv.pdf(distribution)) |
Output :
Distribution : [0. 0.10204082 0.20408163 0.30612245 0.40816327 0.51020408 0.6122449 0.71428571 0.81632653 0.91836735 1.02040816 1.12244898 1.2244898 1.32653061 1.42857143 1.53061224 1.63265306 1.73469388 1.83673469 1.93877551 2.04081633 2.14285714 2.24489796 2.34693878 2.44897959 2.55102041 2.65306122 2.75510204 2.85714286 2.95918367 3.06122449 3.16326531 3.26530612 3.36734694 3.46938776 3.57142857 3.67346939 3.7755102 3.87755102 3.97959184 4.08163265 4.18367347 4.28571429 4.3877551 4.48979592 4.59183673 4.69387755 4.79591837 4.89795918 5. ]
Code #4 : Varying Positional Arguments
from scipy.stats import arcsine import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 1.0, 100) # Varying positional arguments y1 = betaprime.pdf(x, 2.75, 2.75) y2 = betaprime.pdf(x, 3.25, 3.25) plt.plot(x, y1, "*", x, y2, "r--") |
Output :
