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Lemma 24.2: G(Q, θ) ≤ L(θ) for all Q ∈ 𝒬 with equality at the posterior Q_{i,y} = P_θ[Y = y | X = xᵢ], and the maximizers of G(Q, ·) and F(Q, ·) coincide

Proved
UnderstandingML.em_alternate_max

by naimengye · Sep 24, 2026 · Mathlib 0df444a (Lean v4.33.1)

alternate-maximizationexpectation-maximizationjensen-inequality

Lemma 24.2. The EM procedure can be rewritten as Q(t+1)=argmax⁡Q∈QG(Q,θ(t))Q^{(t+1)} = \operatorname{argmax}_{Q \in \mathcal{Q}} G(Q, \theta^{(t)})Q(t+1)=argmaxQ∈Q​G(Q,θ(t)), θ(t+1)=argmax⁡θG(Q(t+1),θ)\theta^{(t+1)} = \operatorname{argmax}_\theta G(Q^{(t+1)}, \theta)θ(t+1)=argmaxθ​G(Q(t+1),θ). Furthermore, G(Q(t+1),θ(t))=L(θ(t))G(Q^{(t+1)}, \theta^{(t)}) = L(\theta^{(t)})G(Q(t+1),θ(t))=L(θ(t)).

Formally, for a positive joint: (i) G(Q,θ)≤L(θ)G(Q, \theta) \le L(\theta)G(Q,θ)≤L(θ) for every Q∈QQ \in \mathcal{Q}Q∈Q (Jensen); (ii) the posterior Qi,y=Pθ[Y=y∣X=xi]Q_{i,y} = P_\theta[Y = y \mid X = x_i]Qi,y​=Pθ​[Y=y∣X=xi​] attains G(Q,θ)=L(θ)G(Q, \theta) = L(\theta)G(Q,θ)=L(θ), hence is a maximizer over Q\mathcal{Q}Q; (iii) for fixed QQQ, G(Q,θ)≤G(Q,θ′)G(Q, \theta) \le G(Q, \theta')G(Q,θ)≤G(Q,θ′) iff F(Q,θ)≤F(Q,θ′)F(Q, \theta) \le F(Q, \theta')F(Q,θ)≤F(Q,θ′).

Preamble
import Definitions.Def_UnderstandingML_Generative

open MeasureTheory ProbabilityTheory
open scoped InnerProductSpace
Formal statement
namespace UnderstandingML

/-- **Lemma 24.2** (p. 351). The EM procedure can be rewritten as
`Q⁽ᵗ⁺¹⁾ = argmax_{Q ∈ 𝒬} G(Q, θ⁽ᵗ⁾)`, `θ⁽ᵗ⁺¹⁾ = argmax_θ G(Q⁽ᵗ⁺¹⁾, θ)`; furthermore
`G(Q⁽ᵗ⁺¹⁾, θ⁽ᵗ⁾) = L(θ⁽ᵗ⁾)`. Stated as: for every `Q ∈ 𝒬`, `G(Q, θ) ≤ L(θ)`; the posterior
`Q_{i,y} = P_θ[Y = y | X = xᵢ]` attains `G(Q, θ) = L(θ)`; and for fixed `Q` the maximizers of
`G(Q, ·)` and `F(Q, ·)` coincide. The joint is positive. -/
theorem em_alternate_max {Θ X : Type*} {k m : ℕ} (p : Θ → X → Fin k → ℝ)
    (hp : ∀ θ x y, 0 < p θ x y) (x : Fin m → X) :
    (∀ (Q : Fin m → Fin k → ℝ) (θ : Θ), IsRowStochastic Q → emG p x Q θ ≤ latentLogLik p x θ) ∧
    (∀ θ : Θ, emG p x (fun i y ↦ posterior p θ (x i) y) θ = latentLogLik p x θ) ∧
    (∀ (Q : Fin m → Fin k → ℝ) (θ θ' : Θ), emG p x Q θ ≤ emG p x Q θ' ↔ emF p x Q θ ≤ emF p x Q θ') := by sorry

end UnderstandingML
Source
Shalev-Shwartz and Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press 2014, doi:10.1017/CBO9781107298019, §24.4.1 pp. 351-352, Lemma 24.2 with its proof
Human review
  • Endorsed by Shuze Chen · Sep 25, 2026

    Confirmed by the moderator at approval.

  • Endorsed by naimengye · Sep 25, 2026

    Confirmed by the mission captain (proposal self-audit).

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