Fubini-Tonelli

Theorem (Fubini-Tonelli)

Let (X,M,μ),(Y,N,ν)(X,\mathscr{M},\mu),(Y,\mathscr{N},\nu ) be σ-finite measure spaces and let f:X×Y→Cf:X\times Y\to\mathbb{C} be P\mathscr{P}-measurable.

  1. Fubini: If f:X×Y→[0,+∞]f:X\times Y\to[0,+\infty], set φ:X→[0,+∞]\varphi:X\to[0,+\infty] and ψ:Y→[0,+∞]\psi:Y\to[0,+\infty] as φ(x)=∫Yfx dν\varphi(x)=\int\limits _{Y}f_{x} \, d\nu and ψ(y)=∫Xfy dμ\psi(y)=\int\limits _{X}f^{y} \, d\mu. Then, φ\varphi is M\mathscr{M}-measurable and ψ\psi is N\mathscr{N}-measurable and we have ∫Xφ dμ=∫Yψ dν\int\limits _{X}\varphi \, d\mu=\int\limits _{Y}\psi \, d\nu That is ∫X∫Yf(x,y) dν(y) dμ(x)=∫Y∫Xf(x,y) dμ(x) dν(y)=∫X×Yf d(μ×ν)\int\limits_{X} \int\limits_{Y}f(x,y) \, d\nu(y) \, d\mu(x) = \int\limits_{Y} \int\limits_{X}f(x,y) \, d\mu(x) \, d\nu(y) =\int\limits _{X\times Y}f \, d(\mu\times \nu)
  2. Tonelli: Let φ∗(x)=∫Y∣fx∣ dν\varphi^{*}(x)=\int\limits _{Y}|f_{x}| \, d\nu. If ∫Xφ∗ dμ<∞\int\limits _{X}\varphi^{*} \, d\mu<\infty then ∫X×Y∣f∣ d(μ×ν)<∞\int\limits _{X\times Y}|f| \, d(\mu \times \nu)<\infty similarly for ψ∗(y)\psi^{*}(y).
  3. If ∫X×Y∣f∣ d(μ×ν)<∞\int\limits _{X\times Y}|f| \, d(\mu \times \nu)<\infty then fx∈L1(ν),fy∈L1(μ)f_{x}\in L^{1}(\nu),f^{y}\in L^{1}(\mu) and ∫X∫Yf(x,y) dν(y) dμ(x)=∫Y∫Xf(x,y) dμ(x) dν(y)\int\limits _{X}\int\limits _{Y}f(x,y) \, d\nu(y) \, d\mu(x)=\int\limits _{Y}\int\limits _{X}f(x,y) \, d\mu(x) \, d\nu(y)

Probability

Theorem (Fubini-Tonelli)

For (E,E,P1),(F,F,P2)(E,\mathcal{E},\mathbb{P}_{1}),(F,\mathcal{F},\mathbb{P}_{2}) Probability Spaces and Product Measure P\mathbb{P} on (E×F,E⊗F,P)(E\times F,\mathcal{E}\otimes \mathcal{F},\mathbb{P}). If f:E×F→Rf: E\times F\to \mathbb{R} is E⊗F\mathcal{E}\otimes \mathcal{F}-measurable and if either f≥0f\ge 0 or f∈L1f\in L^{1} then ∫E×Ff dP=∫E∫Ff dP2 dP1=∫F∫Ef dP1 dP2\int\limits _{E\times F}f \, d\mathbb{P}=\int\limits _{E}\int\limits _{F}f \, d\mathbb{P_{2}} \, d\mathbb{P}_{1}=\int\limits _{F}\int\limits _{E}f \, d\mathbb{P}_{1} \, d\mathbb{P}_{2} with y↦∫Efy(x) dP1(x)y\mapsto \int\limits _{E}f_{y}(x) \, d\mathbb{P}_{1}(x) measurable.

Using this theorem we then covered Convolutions and how allows us to find the distribution of sums of Independent rvs.

Theorem (9.4.5)

Suppose X,YX,Y are Independent rvs with Distributions μ,ν\mu,\nu respectively. Then the distribution of X+YX+Y is given by μ∗ν\mu*\nu, where (μ∗ν)(H)=∫Rμ(H−y) ν(dy).(\mu*\nu)(H)=\int\limits _{\mathbb{R}}\mu(H-y) \, \nu(dy) .Equivalently, assume X,YX,Y have densities f,gf,g, then X+YX+Y has density f∗gf*g.

SDEs

Theorem (Fubini-Tonelli)

Let (X,M,μ),(Y,N,ν)(X,\mathscr{M},\mu),(Y,\mathscr{N},\nu ) be σ-finite measure spaces and let f:(X×Y,P,μ×ν)→(R,B(R))f:(X\times Y,\mathscr{P}, \mu \times \nu)\to(\mathbb{R},\mathcal{B}(\mathbb{R}))be P\mathscr{P}-measurable. Assume ∫X(∫Y∣f(x,y)∣ dν(y)) dμ(x)<∞\int\limits _{X}\left( \int\limits _{Y}|f(x,y)| \, d\nu(y) \right) \, d\mu(x)<\infty (i.e. f∈L2(X×Y,P,μ×ν)f\in L^{2}(X\times Y,\mathscr{P}, \mu \times \nu)) then, ∫X∫Yf(x,y) dν(y) dμ(x)=∫Y∫Xf(x,y) dμ(x) dν(y)=∫X×Yf d(μ×ν)\int\limits_{X} \int\limits_{Y}f(x,y) \, d\nu(y) \, d\mu(x) = \int\limits_{Y} \int\limits_{X}f(x,y) \, d\mu(x) \, d\nu(y) =\int\limits _{X\times Y}f \, d(\mu\times \nu)

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