scipy.stats.bayes_mvs(arr,alpha)函数在给定的贝叶斯置信区间内计算均值,方差和标准差。
参数:
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.
代码1:工作中
# 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 ("\narr1:", arr1)
print ("\narr2:", arr2)
mean, var, std = stats.bayes_mvs(arr1, 0.9)
print ("\nMean of array1:", mean)
print ("\nvar of array1:", var)
print ("\nstd of array1:", std)
mean, var, std = stats.bayes_mvs(arr2, 0.5)
print ("\nMean of array2:", mean)
print ("\nvar of array2:", var)
print ("\nstd of array2:", std)
输出:
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, 27.474130900029646))
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注:本文由纯净天空筛选整理自vishal3096大神的英文原创作品 sciPy stats.bayes_mvs() function | Python。非经特殊声明,原始代码版权归原作者所有,本译文未经允许或授权,请勿转载或复制。