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TheoremStatement: Literature-sourcedProof: AI-adaptedprecheck passaudited 2026-09-04
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  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
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Change of variables for expectation

Statement

Let X:(Ω,F,P)(S,Σ) be a random element, let PX be its law, and let g:(S,Σ)R or g:(S,Σ)C be measurable.

  1. If g0, then E[g(X)]=SgdPX.
  2. If g(X) is integrable, then g is integrable with respect to PX and the same formula holds: E[g(X)]=SgdPX.

Facts & Assumptions

Given: A random element X, its law PX, and a measurable map g as in the Statement.

[L1]

The law PX is a probability measure on (S,Σ) (Law or distribution of a random element, The law of a random element is a probability measure).

[L2]

Measurable outer maps preserve measurability under composition (Composition with a Borel measurable outer map preserves measurability).

[L3]

Every nonnegative measurable function is the increasing limit of nonnegative simple functions, monotone convergence holds, and the nonnegative integral agrees with the simple integral on simple functions (Every nonnegative measurable function is the increasing limit of simple measurable functions, Monotone convergence for the integral, The nonnegative integral agrees with the simple integral on simple functions, The integral of a nonnegative simple function).

[L4]

Expectation is integration against P, and real or complex integrability uses the positive-negative and real-imaginary decompositions (Expectation of a nonnegative or integrable random variable, Integrable real and complex functions, and their integrals).

[L5]

Measurable functions are closed under the elementary operations used by the integral, and the Lebesgue integral is linear on L1 (Closure properties of measurable functions used by the integral, The Lebesgue integral is linear on L1(μ)).

Proof

technique · direct
1.1

By [L2], the composite gX is measurable. If s=j=1mcjχBj is a nonnegative simple function on S with the Bj pairwise disjoint, then sX=j=1mcjχX1(Bj) is a nonnegative simple function on Ω. Using [L1], [L3], and [L4], E[s(X)]=j=1mcjP(X1(Bj))=j=1mcjPX(Bj)=SsdPX.

L1L2L3L4
2.1

Assume now that g0. By [L3], choose nonnegative simple functions sng on S. Then snXgX by step 1.1, so monotone convergence on both spaces and step 1.1 give E[g(X)]=limnE[sn(X)]=limnSsndPX=SgdPX.

step 1.1L3L4
3.1

Assume g(X) is integrable. Then E[g(X)]<, so step 2.1 applied to g gives SgdPX=E[g(X)]<. Hence g is PX-integrable. For real-valued g, [L4] and [L5] give g=g+g with both parts nonnegative, so step 2.1 and linearity yield E[g(X)]=E[g+(X)]E[g(X)]=Sg+dPXSgdPX=SgdPX.

step 2.1L4L5
4.1

If g is complex-valued, write g=u+iv with real measurable parts u,v. The inequality u,vg and step 3.1 show that u(X) and v(X) are integrable, so the real-valued case applied to u and v, followed by complex-linearity from [L5], gives E[g(X)]=E[u(X)]+iE[v(X)]=SudPX+iSvdPX=SgdPX.

step 3.1L4L5
5.1

Step 2.1 proves the nonnegative case, while steps 3.1 and 4.1 prove the integrable real and complex cases.

step 2.1step 3.1step 4.1

Depends on

Used by

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Sources