Bienaymé's identity

In probability theory, the general form of Bienaymé's identity, named for Irénée-Jules Bienaymé, states that Var ⁡ ( ∑ i = 1 n X i ) = ∑ i = 1 n Var ⁡ ( X i ) + 2 ∑ i , j = 1 i < j n Cov ⁡ ( X i , X j ) = ∑ i , j = 1 n Cov ⁡ ( X i , X j ) . {\displaystyle {\begin{aligned}\operatorname {Var} \left(\sum _{i=1}^{n}X_{i}\right)&=\sum _{i=1}^{n}\operatorname {Var} (X_{i})+2\sum _{i,j=1 \atop i<j}^{n}\operatorname {Cov} (X_{i},X_{j})\\&=\sum _{i,j=1}^{n}\operatorname {Cov} (X_{i},X_{j}).\end{aligned}}} This can be simplified if X 1 , … , X n {\displaystyle X_{1},\ldots ,X_{n}} are pairwise independent or just uncorrelated, integrable random variables, each with finite second moment.

Source: Wikipedia — Bienaymé's identity (CC BY-SA 4.0)

Bienaymé's identity

In probability theory, the general form of Bienaymé's identity, named for Irénée-Jules Bienaymé, states that Var ⁡ ( ∑ i = 1 n X i ) = ∑ i = 1 n Var ⁡ ( X i ) + 2 ∑ i , j = 1 i < j n Cov ⁡ ( X i , X j ) = ∑ i , j = 1 n Cov ⁡ ( X i , X j ) . {\displaystyle {\begin{aligned}\operatorname {Var} \left(\sum _{i=1}^{n}X_{i}\right)&=\sum _{i=1}^{n}\operatorname {Var} (X_{i})+2\sum _{i,j=1 \atop i<j}^{n}\operatorname {Cov} (X_{i},X_{j})\\&=\sum _{i,j=1}^{n}\operatorname {Cov} (X_{i},X_{j}).\end{aligned}}} This can be simplified if X 1 , … , X n {\displaystyle X_{1},\ldots ,X_{n}} are pairwise independent or just uncorrelated, integrable random variables, each with finite second moment.

Source: Wikipedia "Bienaymé's identity" · CC BY-SA 4.0

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