scipy stats.beta () | 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 continuous random variable

Code # 1: Generate beta continuous random variable values ​​

# scipy import

from scipy.stats import beta

 

numargs = beta.numargs

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

rv = beta (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 = beta.rvs (a, b, scale = 2 , size = 10 )

p rint ( "Random Variates:" , R)

 
# PDF

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

print ( "Probability Distribution:" , R)

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 0.779037 62 0.8752367 1.1264383] 

Code # 3: Graphic representation.

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))

Output:

Code # 4: Various Positional Arguments

Output:


from scipy.stats import arcsine

import matplotlib.pyplot as plt

import numpy as np

 

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

 
# Various 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--" )