scipy stats.burr () | 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: burr continuous random variable

Code # 1: Generating a continuous random variable

       # scipy import  
   from   scipy.stats   import   burr 
   
   numargs   =   burr.numargs 
   [a, b]   =   [  0.6  ,]   *   numargs 
   rv   =   burr (a, b) 
    
   print   (  "RV:"  , rv)

Output:

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

Code # 2: beta random variations and probability distribution function.

import numpy as np

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

# Random Variants

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

p rint ( "Random Variates:" , R)

# PDF

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

print ( "Probability Distribution:" , R)

Output:

 Random Variates: [1.51241629e-04 3.47964171e-01 2.94154949e-02 5.10430246e-02 1.82413279e-02 2.12564883e + 00 3.51099766e-05 2.32907895e + 01 6.24723647e-04 2.79124934e-01] Probability Distribution: [6.21994723 1.01375434 0.57575653 0.40021455 0.30462819 0.24439598 0.20298921 0.17281591 0.14988693 0.1319016]

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

import matplotlib. pyplot as plt

import numpy as np

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

# Various positional arguments

y1 = burr.pdf (x, 2.75 , 2.75 )

   y2   =   burr.pdf (x,   3.25  ,   3.25  ) 
   plt.plot (x, y1,   " * " , x, y2,  " r-- " )

Output:

scipy stats.burr () | python

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