Scipy stats.beta Function

the
scipy.stats.beta()
Function  Beta Continuous Random Variable
 Beta Random Variates and Probability Distribution Function
Beta distribution in statistics is defined as a group of consecutive probability distributions defined between the interval [0,1]. The beta distribution has two parameters known as shape parameters. These shape parameters are denoted by α
and β
that control the shape of the whole distribution and represent a random variable’s exponents.
the scipy.stats.beta()
Function
The scipy.stats.beta()
function of the SciPy
library is a beta continuous random variable defined with various shape parameters and a standard format to complete the function’s specifications properly.
Following are the parameters of the scipy.stats.beta
function.
q 
It defines the upper and lower end tail of the probability. 
a, b 
It defines the shape parameters of the function. 
x 
It defines the quantiles. 
loc 
It defines the location parameter of the function. The default value of this function is 0 . 
scale 
The default value of the scale parameter is 1 . 
size 
It is defined in the form of a tuple of integers. It defines the shape of random variates. 
moments 
It is defined by letter i.e, msvk , where m = mean , v = variance , s = Fisher's skew , and k = Fisher's kurtosis . 
All the parameters except q
, a,b
, and x
are optional. That means it is unnecessary to define them every time while using the scipy.stats.beta
function.
There are various methods to define the scipy.stats.beta
function:
rvs(a, b, loc=0, scale=1, size=1, random_state=None)
 This method is used whenever there is a need to findrandom variates
.pdf(x, a, b, loc=0, scale=1)
 This method is known as theprobability density function
cdf(x, a, b, loc=0, scale=1)
 This method is known as thecummulative distribution function
logcdf(x, a, b, loc=0, scale=1)
 This method finds thelog
of thecummulative distribution function
.
There are many more such methods to define the scipy.stats.beta
function. But in every method, the value of the parameters varies.
Beta Continuous Random Variable
from scipy.stats import beta
num_args = beta.numargs
[a, b] = [1.2, ] * num_args
random_var = beta(a, b)
print ("Random Variable : ", random_var)
Output:
Random Variable : <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f9a6b366af0>
Beta Random Variates and Probability Distribution Function
In this example, the arange
function of the NumPy
library is used. This is a builtin function of the NumPy
library that helps return an array object with a specific number of values with a definite spacing.
import numpy as np
quantile_val = np.arange (0.1, 1, 0.2)
rv = beta.rvs(a, b, scale = 2, size = 10)
print ("Random Variates : ", rv)
rv_pdf = beta.pdf(quantile_val, a, b, loc = 0, scale = 1)
print ("Probability Distribution : ", rv_pdf)
Output:
Random Variates : [0.33734047 1.72002734 1.67064615 0.72633407 0.71346865 0.81301286
1.39419329 0.65489343 0.97953887 1.15867132]
Probability Distribution : [0.91029949 1.07839945 1.11666731 1.07839945 0.91029949]