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  2. sciPy function stats.bayes_mvs () | python

sciPy function stats.bayes_mvs () | python

scipy.stats.bayes_mvs (arr, alpha) function calculates mean, variance and standard deviation in a given Bayesian confidence interval.

Parameters:
arr: [array_like] The input data can be multidimensional but will be flattened before use.
alpha: Probability that the returned confidence interval contains the true parameter.

Results: mean, variance and standard deviation in the given Bayesian confidence interval.

Code # 1: Work

# stats.bayes_mvs () method

import numpy as np

from scipy import stats

 

arr1 = [[ 20 , 2 , 7 , 1 , 34 ],

[ 50 , 12 , 12 , 34 , 4 ]]

 

arr2 = [ 50 , 12 , 12 , 34 , 4 ]

 

print ( "arr1:" , arr1)

print ( "arr2:" , arr2)

 

mean, var, std = stats.bayes_mvs (arr1, 0.9 )

 

print ( " Mean of array1: " , mean) < / code>

print ( "var of array1:" , var)

print ( "std of array1:" , std)

  

mean, var, std = stats.bayes_mvs (arr2, 0.5 )

 

print ( " Mean of array2: " , mean)

print ( "var of array2:" , var)

print ( " std o f array2: " , std)

  

Output:

arr1: [[ 20, 2, 7, 1, 34], [50, 12, 12, 34, 4]]

arr2: [50, 12, 12, 34, 4]

Mean of array1: Mean (statistic = 17.6, minmax = (7.99212522273964, 27.207874777260358))

var of array1: Variance (statistic = 353.2, minmax = (146.13176149159307, 743.5537128176551))

std of array1: Std_dev (statistic = 18.136411760663574, minmax = (12.088497073316974, 27.26818132581737))

Mean of array2: Mean (statistic = 22.4, minmax = (16.090582413339323, 28.709417586660674)) var of array2: Variance (statistic = 725.6, minmax = (269.47585801746374, 754.8278687119639))

std of array2: Std_dev (statistic = 23.872262300862655, minmax = (16.415719844632576, 27.4764) >