Law of total covariance

In probability theory, the law of total covariance, covariance decomposition formula, or conditional covariance formula states that if X, Y, and Z are random variables on the same probability space, and the covariance of X and Y is finite, then cov ⁡ ( X , Y ) = E ⁡ ( cov ⁡ ( X , Y ∣ Z ) ) + cov ⁡ ( E ⁡ ( X ∣ Z ) , E ⁡ ( Y ∣ Z ) ) . {\displaystyle \operatorname {cov} (X,Y)=\operatorname {E} (\operatorname {cov} (X,Y\mid Z))+\operatorname {cov} (\operatorname {E} (X\mid Z),\operatorname {E} (Y\mid Z)).} The nomenclature in this article's title parallels the phrase law of total variance.

Source: Wikipedia — Law of total covariance (CC BY-SA 4.0)

Law of total covariance

In probability theory, the law of total covariance, covariance decomposition formula, or conditional covariance formula states that if X, Y, and Z are random variables on the same probability space, and the covariance of X and Y is finite, then cov ⁡ ( X , Y ) = E ⁡ ( cov ⁡ ( X , Y ∣ Z ) ) + cov ⁡ ( E ⁡ ( X ∣ Z ) , E ⁡ ( Y ∣ Z ) ) . {\displaystyle \operatorname {cov} (X,Y)=\operatorname {E} (\operatorname {cov} (X,Y\mid Z))+\operatorname {cov} (\operatorname {E} (X\mid Z),\operatorname {E} (Y\mid Z)).} The nomenclature in this article's title parallels the phrase law of total variance.

Source: Wikipedia "Law of total covariance" · CC BY-SA 4.0

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