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TheoremStatement: AI-adaptedProof: AI-adaptedPipeline-generatedjudge pass (gpt-6-luna)
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Independent product inputs have an orthogonal Hoeffding decomposition

Statement

Let d≥1 be finite, let X=(X1,…,Xd) have a finite product probability law, and let f(X)∈L2. With the components of Product-law Hoeffding ANOVA components, f=∑u⊆[d]fuin L2, each fu depends only on Xu, every nonempty component has mean zero, and for every i∈u its coordinate average satisfies Pu∖{i}fu=0. Distinct components are orthogonal, and Var⁡(f(X))=∑∅≠u⊆[d]E[fu(Xu)2].

Facts & Assumptions

Given: A finite product probability law, a real f∈L2, and the components fu defined above.

[F1]

Product-coordinate averaging operators: Pv averages over coordinates outside v and lifts the result to the product space.

[F2]

Product-coordinate projections commute and contract in L2: PJPK=PJ∩K, PJ is self-adjoint, and PJ2=PJ.

[F3]

Product-law Hoeffding ANOVA components: fu=∑v⊆u(−1)∣u∣−∣v∣Pvf.

[F4]

Moments, variance, and covariance on a probability space: variance is the squared L2 norm after subtracting the mean.

Proof

Given: The objects in the Statement and facts [F1]–[F4].

1.1F1F3algebra

Interchange the two finite sums in ∑u⊆[d]fu. The coefficient of Pvf is ∑u⊇v(−1)∣u∣−∣v∣=(1−1)d−∣v∣, which is zero unless v=[d], when it is one. Since P[d]f=f, this proves reconstruction.

2.1F1F2F3step 1.1algebra

Fix nonempty u and i∈u. Apply Pu∖{i} to the finite sum defining fu. For each w⊆u∖{i}, exactly the terms v=w and v=w∪{i} both become Pwf by [F2]; their signs are opposite. All terms cancel, so Pu∖{i}fu=0. Also Pufu=fu, since every v⊆u satisfies PuPv=Pv. Thus fu depends only on u, and for nonempty u its mean P∅fu is zero by applying [F2] to the preceding identity.

3.1F2step 2.1choose

Let u≠w. Choose i in their symmetric difference; after interchanging their names if needed, take i∈u∖w, and put Q=P[d]∖{i}. Since Pufu=fu, the projection composition rule [F2] and step 2.1 give Qfu=QPufu=Pu∖{i}fu=0. Since Pwfw=fw and i∉w, the same rule gives Qfw=QPwfw=Pwfw=fw. Self-adjointness of Q therefore gives ⟨fu,fw⟩=⟨fu,Qfw⟩=⟨Qfu,fw⟩=0.

4.1F3F4step 1.1step 3.1algebra∎

By step 1.1, f−Ef=∑u≠∅fu. Step 3.1 makes this a finite orthogonal sum, so its squared L2 norm is the sum of the squared component norms. Apply [F4] to identify that norm with Var⁡(f(X)).

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Sources