scipy stats.beta() | Python

scipy.stats.beta() is an beta 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 continuous random variable

Code #1 : Creating beta continuous random variable

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

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Output :

RV : 
 <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000029482FCC438>

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

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import numpy as np
quantile = np.arange (0.01, 1, 0.1)
   
# Random Variates
R = beta.rvs(a, b, scale = 2,  size = 10)
print ("Random Variates : \n", R)
  
# PDF
R = beta.pdf(quantile, a, b, loc = 0, scale = 1)
print ("\nProbability Distribution : \n", R)

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Output :

Random Variates : 
 [1.47189604 1.47284574 1.84692416 1.0686604  0.32709236 1.96857076
 0.00639731 1.97093898 1.34811881 0.34269426]

Probability Distribution : 
 [2.62281037 1.04883674 0.84934164 0.76724957 0.73040985 0.72096547
 0.73529768 0.77903762 0.8752367  1.1264383 ]

Code #3 : Graphical Representation.

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import numpy as np
import matplotlib.pyplot as plt
  
distribution = np.linspace(0, np.maximum(rv.dist.b, 5))
plot = plt.plot(distribution, rv.pdf(distribution))

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Output :

Code #4 : Varying Positional Arguments

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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 = beta.pdf(x, 2.75, 2.75)
y2 = beta.pdf(x, 3.25, 3.25)
plt.plot(x, y1, "*", x, y2, "r--")

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Output :




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