scipy stats.betaprime () | python



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: Generating a continuous random betaprime values ​​

# scipy import

from scipy.stats import betaprime

 

numargs = betaprimeprime.numargs

[a, b] = [ 0.6 ,] * numargs

rv = betaprimeprime (a, b)

 

print ( "RV:" , rv)

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x0000029482FCC438 & gt ; 

Code # 2: Beta Simple Random Variations and Probability Distribution.

import numpy as np

quantile = np.arange ( 0.01 , 1 , 0.1 )

 
# Random Variants

R = betaprime.rvs (a, b, scale = 2 , size = 10 )

print ( " Random Variates: " , R)

 
# PDF

R = betaprime.pdf (quantile, a, b, loc = 0 , scale = 1 )

print ( "Probability Distribution:" , 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 0.2849 0363 0.24941484 0.22101038 0.1977718] 

Code # 3: Graphic representation.

import numpy as np

import matplotlib.pyplot as plt

 

distribution = np.linspace ( 0 , np.minimum (rv.dist. b, 5 ))

print ( "Distribution:" , 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: Various Positional Arguments

Output:


from scipy. stats import arcsi ne

import matplotlib.pyplot as plt

import numpy as np

 

x = np.linspace ( 0 , 1.0 , 100 )

 
# Various 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--" )