sciPy function stats.bayes_mvs () | python

Michael Zippo 18.07.2021

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) >