Doob–Dynkin lemma

In probability theory, the Doob–Dynkin lemma, named after Joseph L. Doob and Eugene Dynkin, characterizes the situation when one random variable is a function of another by the inclusion of the \sigma generated by the random variables. The usual statement of the lemma is formulated in terms of one random variable being measurable with respect to the \sigma-algebra generated by the other.

The lemma plays an important role in the conditional expectation in probability theory, where it allows to replace the conditioning on a random variable by conditioning on the \sigma-algebra that is generated by the random variable.

Statement of the lemma

Let \Omega be a sample space. For a function f:\Omega \rightarrow R^n, the \sigma-algebra generated by f is defined as the family of sets f^{-1}(S), where S are all Borel sets.

Lemma Let X,Y: \Omega \rightarrow R^n be random elements and \sigma(X) be the \sigma algebra generated by X. Then Y is \sigma(X)-measurable if and only if Y=g(X) for some Borel measurable function g:R^n\rightarrow R^n.

The "if" part of the lemma is simply the statement that the composition of two measurable functions is measurable. The "only if" part is the nontrivial one.

By definition, Y being \sigma(X)-measurable is the same as Y^{-1}(S)\in \sigma(X) for any Borel set S, which is the same as \sigma(Y) \subset \sigma(X). So, the lemma can be rewritten in the following, equivalent form.

Lemma Let X,Y: \Omega \rightarrow R^n be random elements and \sigma(X) and \sigma(Y) the \sigma algebras generated by X and Y, respectively. Then Y=g(X) for some Borel measurable function g:R^n\rightarrow R^n if and only if \sigma(Y) \subset \sigma(X).

References

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