ProofComplete

Product Measures and Fubini - Key Proof

ProofProof Sketch of Fubini's Theorem

We outline the proof of Fubini's Theorem for integrable functions. The complete proof requires several steps building from simple to general functions.

Given: fL1(X×Y,μν)f \in L^1(X \times Y, \mu \otimes \nu)

Step 1 (Indicator functions): For E=A×BE = A \times B where AFA \in \mathcal{F} and BGB \in \mathcal{G}: X×YχEd(μν)=(μν)(A×B)=μ(A)ν(B)\int_{X \times Y} \chi_E \, d(\mu \otimes \nu) = (\mu \otimes \nu)(A \times B) = \mu(A) \nu(B)

The iterated integrals give: X(YχE(x,y)dν(y))dμ(x)=XχA(x)ν(B)dμ(x)=μ(A)ν(B)\int_X \left(\int_Y \chi_E(x, y) \, d\nu(y)\right) d\mu(x) = \int_X \chi_A(x) \nu(B) \, d\mu(x) = \mu(A) \nu(B)

Thus Fubini holds for indicators of rectangles.

Step 2 (Simple functions): By linearity, Fubini holds for simple functions s=i=1naiχEis = \sum_{i=1}^{n} a_i \chi_{E_i} where EiE_i are finite unions of rectangles.

Step 3 (Non-negative functions): For non-negative measurable ff, approximate by increasing simple functions snfs_n \uparrow f. By the Monotone Convergence Theorem applied three times (once for each integral), Fubini holds for ff. This is essentially Tonelli's Theorem.

Step 4 (Integrable functions): For general fL1f \in L^1, write f=f+ff = f^+ - f^- where f+=max(f,0)f^+ = \max(f, 0) and f=max(f,0)f^- = \max(-f, 0). Both f+f^+ and ff^- are non-negative and integrable.

By Step 3, Fubini holds for f+f^+ and ff^-. By linearity: X×Yfd(μν)=X×Yf+d(μν)X×Yfd(μν)\int_{X \times Y} f \, d(\mu \otimes \nu) = \int_{X \times Y} f^+ \, d(\mu \otimes \nu) - \int_{X \times Y} f^- \, d(\mu \otimes \nu)

Applying Fubini to each term: =X(Yf+(x,y)dν(y))dμ(x)X(Yf(x,y)dν(y))dμ(x)= \int_X \left(\int_Y f^+(x, y) \, d\nu(y)\right) d\mu(x) - \int_X \left(\int_Y f^-(x, y) \, d\nu(y)\right) d\mu(x)

By linearity of integration: =X(Yf(x,y)dν(y))dμ(x)= \int_X \left(\int_Y f(x, y) \, d\nu(y)\right) d\mu(x)

Remark

Key insights in the proof:

  1. Build from simple to complex: The proof follows the standard measure theory paradigm: indicators → simple functions → non-negative functions → general functions.

  2. MCT is essential: The Monotone Convergence Theorem allows passing limits through integrals in Step 3.

  3. σ\sigma-finiteness matters: The uniqueness of product measure (needed for Step 1) requires σ\sigma-finiteness.

  4. Measurability of sections: A subtle point is showing that xYf(x,y)dν(y)x \mapsto \int_Y f(x, y) \, d\nu(y) is measurable. This uses the fact that the map is measurable for simple functions and is preserved under monotone limits.