| dbo:abstract |
In statistics, inverse-variance weighting is a method of aggregating two or more random variables to minimize the variance of the weighted average. Each random variable is weighted in inverse proportion to its variance, i.e. proportional to its precision. Given a sequence of independent observations yi with variances σi2, the inverse-variance weighted average is given by The inverse-variance weighted average has the least variance among all weighted averages, which can be calculated as If the variances of the measurements are all equal, then the inverse-variance weighted average becomes the simple average. Inverse-variance weighting is typically used in statistical meta-analysis or sensor fusion to combine the results from independent measurements. (en) 统计学中，逆方差加权（英語：inverse-variance weighting）是一种对随机变量测量值进行加权平均的方法。每个随机变量被其方差的倒数加权。该方法可使平均值的方差最小。 若随机变量的一系列独立测量值为yi，其方差为σi2，则这些测量值的逆方差加权平均为 在所有加权平均方法中，逆方差加权平均的方差最小，为 若各测量值的方差相等，则逆方差加权平均与简单平均相同。 逆方差加权通常在元分析中用来整合独立测量的结果。 (zh) |
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| rdfs:comment |
统计学中，逆方差加权（英語：inverse-variance weighting）是一种对随机变量测量值进行加权平均的方法。每个随机变量被其方差的倒数加权。该方法可使平均值的方差最小。 若随机变量的一系列独立测量值为yi，其方差为σi2，则这些测量值的逆方差加权平均为 在所有加权平均方法中，逆方差加权平均的方差最小，为 若各测量值的方差相等，则逆方差加权平均与简单平均相同。 逆方差加权通常在元分析中用来整合独立测量的结果。 (zh) In statistics, inverse-variance weighting is a method of aggregating two or more random variables to minimize the variance of the weighted average. Each random variable is weighted in inverse proportion to its variance, i.e. proportional to its precision. Given a sequence of independent observations yi with variances σi2, the inverse-variance weighted average is given by The inverse-variance weighted average has the least variance among all weighted averages, which can be calculated as (en) |
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Inverse-variance weighting (en) 逆方差加权 (zh) |
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